Income Inequality, Economic Growth, and the Effect of

Redistribution

Klaus Grundler* and Philipp Scheuermeyer*

*University of Wurzburg, Department of Economics, Sanderring 2, 97070

Wurzburg. E-Mail: klaus.gruendler@uni-wuerzburg.de,

philipp.scheuermeyer@uni-wuerzburg.de.

October 2014

Abstract

Evidence from a large set of well comparable worldwide data provided in the SWIIDshows a robust negative correlation between income inequality and growth that can betraced back to three essential transmission channel. More unequal societies tend tohave a less educated population, higher fertility rates, and—to some extent—lowerinvestment shares. All of these effects are harmfull for growth and reinforced by a lim-ited availability of credit. Higher public spending on education attenuates the negativeeffects of inequality.

Less evidence is found for a negative growth effect of public redistribution, measuredvia the pre-post approach. While redistribution hampers investments, its negativeincentive effects on average do not seem to transmit to economic growth. Combinedwith the resulting decrease in net inequality, we find evidence that redistribution isbeneficial for growth if market inequality is high. However, such an effect cannot befound at lower levels of initial market inequality.

The results stem from developing and middle-income countries where the negativepotential of inequality is most severe, yielding a socio-political environment whereequality of opportunities is much less pronounced than in advanced economies. Inadvanced economies, we do not detect any significant correlation between inequalityand growth.

Keywords: Economic Growth, Redistribution, Inequality

JEL No.: O11, O15, O47, H23

1

1 Introduction

The United States are among the most unequal economies in the world. The Gini coefficient

of disposable income inequality amounts to almost 40 percent, indicating a level of inequality

that no other developed country in the world exceeds. However, the United States also

possess one of the most affluent economies on the globe, where per capita incomes are

four times as high as the average income of the countries in the world. Inequality and

prosperity sometimes go hand in hand with each other. In his famous 1975 book “Equity

and Efficiency: The Big Tradeoff”, Arthur Okun points out that the tradeoff between social

justice and economic efficiency “plagues us in dozens of dimensions of social policy”.1

Okuns notion reflects the traditional view in economics, assuming that income inequality

is beneficial for growth and—above all— that public redistribution via taxes and transfers

creates disincentives and inefficiencies which Okun compares to a “leaky bucket”, losing

money whenever transfers are made from the rich to the poor. Yet, the empirical evidence

on the existence of such a trade-off is rather weak. Indeed, aside from the previous example

of the United States, there is a number of affluent countries with exceptionally low levels of

inequality (e.g. Denmark, Sweden, Austria, Luxembourg).

The empirical literature that is concerned with the question on how inequality affects

the prosperity of nations can be divided into two more or less independent strands: The first

strand examines the link between inequality and growth, coming to a tentative consensus

that a low level of inequality is good for growth, at least in the long-run. The second branch

studies the growth effects of redistributive fiscal policies, especially taxes and social transfers.

This paper follows a novel approach by simultaneously exploring the growth effects of both

variables, examining on the one hand the “direct” growth effect of redistribution and on the

other hand the “indirect” effect of the resulting net inequality. By this means we can not

only evaluate whether and why lower levels of inequality may be good or bad for growth,

but also to which extent governments affect growth by taking redistributive measures.

There is a rich and rapidly growing empirical literatur that is concerned with the effects

of inequality on growth: Earlier studies based on cross-country growth regressions evolved

into a consensus on a negative relationship between initial inequality and the growth rate

in subsequent periods.2 However, with the advent of panel data estimations, the results

became more inconclusive. Particularly Li and Zou (1998) and Forbes (2000) contradict the

earlier results by finding a positive inequality-growth relationship based on fixed-effects and

first-differences (Arellano-Bond) estimations. Likewise, the simultaneous equation models

applied by Barro (2000) yield a negative impact of inequality in developing economies,

whereas the influence in advanced economies is insignificant. The study further brings to

light a crucial entanglement of inequality and the fertility rate, as the significantly negative

impact of inequality on growth in the whole sample estimations emerges only if the fertility

1See Okun (1975).2The empirical growth literature of the 1990s is comprehensively reviewed in Aghion et al. (1999) and

Benabou (1996).

2

rate is omitted. Voitchovsky (2005) enriches the debate by emphasizing the importance

of the shape of the income distribution, but receives an overly negative effect of the Gini

coefficient by applying a system GMM estimator. A very recent paper by Halter et al.

(2014) explains the contradicting results of earlier studies by the choice of different empirical

methods, which emphasize different time horizons of the inequality growth relation. It

stresses that estimates based on time-series variations are likely to pick up positive short-

run effects of inequality, while methods which also exploit cross-country variations reflect

the negative impact of inequality in the medium to long-run. A couple of studies have

also examined the transmission channels by which inequality effects growth, mostly based

on cross-country variations (see, e.g. Perotti (1994, 1996)). However, with the exception of

Barro (2000), to the best of our knowledge, only few studies are focusing on the transmission

process with the help of panel data.

A further explanation of the substantial differences in the results concerning the impact

of inequality on growth that previous studies brought to light is the scarcity of convinc-

ing data on inequality. Most studies struggle with a rather low number of observations,

which results in several empirical drawbacks. First, with only few degrees of freedom, the

econometric specification can incorporate only few covariates, which may yield an omitted

variable bias and thus inconsistency in the estimated coefficients. Second, as many previous

data sets only contain a small fraction of countries, a sample selection bias may distort the

results. Especially since different data sets contain inequality measures for altering subsets

of countries, albeit with a clear tendency to neglect developing economies. A further problem

is the time dimension: Most of the time series regarding inequality measures are interrupted

by large periods where no data is available. This complicates the use of panel data methods

considerably. All of these disadvantages force researchers to compromise, which can easily

result in deviations of the outcomes.

Regarding the growth effects of redistributive fiscal policy, Easterly and Rebelo (1993)

and Perotti (1996) find if anything a positive growth effect of various policy instruments

such as average or marginal tax rates, and different types of social spending. Tanzi and Zee

(1997) survey the literature on the long run effects of fiscal policy in general and conclude

that empirical studies are rather disappointing in its support of the theoretical predictions

from the endogenous growth theory. This is broadly in line with Lindert (2004) who finds

that large welfare states have come up with methods to minimize the negative growth

impact of social spending. In contradiction, a recent study by Muinelo-Gallo and Roca-

Sagales (2013) shows that distributive expenditures and direct taxes deter growth in a panel

of 21 high-income OECD countries.

A general problem concerning the judgment of the growth effects of redistribution based

on the above mentioned literature is that it remains unclear in how far the examined fiscal

policy instruments are indeed redistributive. Thus, this study contributes to the literature

by following a fairly novel approach: Growth rates are regressed on effective redistribution

instead of individual elements of redistributive fiscal policy. A new dataset by Solt (2009) al-

3

lows us to distinguish neatly between the Gini coefficients of market and net income, thereby

permitting the calculation of a variable of overall redistribution for a large number of coun-

tries and years. Moreover, it allows us to study the growth effects of net income inequality

while controlling for public redistribution. Similarly, the overall effects of redistribution are

assessed when market inequality is held constant. Applying the Blundell-Bond system GMM

estimator—and Simultaneous Equation Models within the sensitivity analysis—this study

indicates a negative inequality growth relationship, while growth appears to be surprisingly

insensitive to redistribution. Owing to the advantages in data availability, our dataset is

considerably extended compared to earlier studies: Based on a maximum of 962 observations

from 152 countries between 1960 and 2012 we can now also account for the exceptionally

high growth rates in many developing countries during the last decade. The exceptional size

of the dataset not only provides a remedy for some of the above mentioned problems in the

empirical literature, but it allows us to distinguish between several subsamples split by the

development level or data quality. The clean distinction between inequality of market and

disposable income enables some new perspectives on the transmission channels of inequality

on growth. This examination is indeed fruitful, as it turns out that inequality acts through

a variety of transmission channels, such as education, fertility, and investment. Holding

constant these channels, the direct effect of inequality on growth vanishes.

Two recent studies have applied a similar approach as we did. Thewissen (2013) calcu-

lates a measure of effective redistribution from data of the Luxembourg Income Study and

the OECD. In a panel of exclusively high-income countries, no robust association between

inequality and growth or redistribution and growth can be found, while there is some in-

dication for a positive relation between top income shares and growth. Berg et al. (2014)

are using the preceding version of the SWIID in a study on redistribution, inequality and

growth. The focus is on the impact of redistribution on medium term growth but also on

growth spells duration, which appears to be small or non-existent. Regarding the inequality-

growth nexus they find a negative relationship even when a number of control variables are

held constant. Yet in contrast to the present paper, Berg et al. (2014) do not focus on the

specific transmission channels of income inequality.

The paper is organized as follows: Section 2 briefly reviews the main theories on in-

equality, redistribution and growth, setting the groundwork for the empirical investigation

of the relationship and its transmission channels. Section 3.1 presents our empirical model

and estimation technique. Section 3.2 describes the data used in our regressions with a

distint focus on the data concerning inequality and redistribution. In addition, some cru-

cial relationships between the covariates, as well as the link between market inequality and

redistribution are examined. The regression results follow in Section 4. After the illustra-

tion of the baseline findings, we conduct a sensitivity analysis using different estimation

techniques in order to guarantee that the results are not primarily driven by the selected

empirical method. Subsequently, we examine the overall effects of public redistribution,

and investigate the transmission channels through wich inequality assumes its influence on

4

growth. The empirical section closes with an examination of the effects of inequality and

redistribution for different levels of development. We conclude in Section 5.

2 Theoretical link between inequality, redistribution,

and economic growth

Numerous papers have been written on the theoretical link between inequality and economic

growth. In this section we aim to illustrate the most convincing theories proposed by

economic literature which we have consolidated into five categories.3 By this means we are

setting the groundwork for the following detailed examination of the theoretical transmission

channels in our empirical regressions. A crucial distinction can be made between the first

four and the last argument: While most of the arguments are related to the distribution

of disposable income (or wealth), the final argument is related to market income and the

growth effects of public redistribution. In constrast to most of the recent studies, it is

our distinct purpose to give the latter argument a considerable amount of attention in the

empirical investigation.

2.1 Differential Saving Rates

According to the well-known Kaldor (1955) hypothesis, the marginal propensity to save

is positively correlated with the income level of individual households. Based on this as-

sumption, Bourguignon (1981) shows that with a convex saving function, a more uneven

distribution of income leads to higher aggregate savings which can be channeled into in-

vestments that are conducive to growth. However, from a Keynesian perspective the rise in

savings by the rich can also lead to a structural drag on demand and to increasing financial

instability [see for example Stiglitz (2012)].

2.2 Sociopolitical Unrest

A further channel through which inequality affects economic growth is emphasized by Alesina

and Perotti (1996) and Alesina et al. (1996) and concerns the extent of socio-political sta-

bility. Political stability increases policy uncertainty, which has negative effects on a variety

of productive economic decisions such as the investment in human or physical capital, or

the saving rate. As income inequality increases political instability, it indirectly leads to

a reduction of the growth rate. Especially the absence of a wealthy middle class enhances

the vulnerability to instability. A high probability of a future government change increases

uncertainty, which initiates capital exports as investments abroad may be associated with a

3A review on the perspective of the new growth theories can be found in Aghion et al. (1999). Voitchovsky(2005) and Ehrhart (2009) are covering both the theoretical and empirical literature. Neves and Silva (2014)are focusing primarily on the empirical literature.

5

lower risk. At the same time, foreign investors will be discouraged to invest in a country if

the political and economic conditions are fragile.

On the other hand, high rates of inequality may also produce social instability. Particu-

larly if inequality is accompanied by low rates of social mobility, individuals may make use

of illegal sources of income, as their impression—subjective or not—may be that chances

in the regular labor market are low. Investments in human capital will thus be deferred

or substituted, which negatively affects economic growth. Enhancing crime rates may also

lead to political instability. In either case, it will prevent foreign and domestic investors to

invest in the country, which decreases capital accumulation and deteriorates the situation

in the labor market.

One further argument that is related to the socio-political environment is the existence

of crony capitalism and nepotism. In societies with a highly unequal distribution of incomes,

especially due to an exorbitantly wealthy upper class, the rich may have manifold possibilities

for political influence. The more unequal the income distribution, the lower may be the

opposition to such actions. However, if the government devotes itself disproportionately

to the interests of the rich, one can expect a variety of efficiency losses for the economy,

especially with respect to factor allocation and human capital investments.

2.3 Credit Market Imperfections, Unequal Opportunities and In-

vestment Indivisibilities

An equable distribution of wealth or disposable income can be growth enhancing if marginal

returns of human capital and physical investment are decreasing and credit markets are

imperfect. One line of this argument goes back to Stiglitz (1969) and is modified by Aghion

et al. (1999). It essentially assumes people as potential entrepreneurs facing individual in-

vestment projects that are bound by decreasing marginal returns.4 If capital markets are

limited, the poor will not be able to exploit their opportunities, while the wealthy are overin-

vesting in their own projects so that marginal returns are relatively low. Wealth distribution

should thus affect the average productivity of physical investment in the aggregate economy,

while its quantity may be relatively unaffected. Another line of reasoning has been brought

forward by Galdor and Zeira (1993) and is focused on human capital investment. An un-

equal distribution of parental wealth will prevent some individuals from exploiting their full

intellectual potential via formal education if the direct costs of education or—more relevant

in developing economies— the opportunity costs of forgone income, cannot be covered by

credit or the government. As the maximum amount of human capital investment per capita

is limited, at least in terms of years of formal education, inequality is likely to affect both

the quantity and average productivity of human capital investment in aggregate. Naturally,

educational inequality and its negative growth effect can also be directly mitigated by a

4Piketty (1997, 2000) additionally stresses the role of investment indivisibilities. In the absence of mini-mum set-up costs for investments, poorer families could be gradually catching up by investing in larger andlarger projects.

6

public education system which provides free and high quality education to children from

poor families.

Instead of decreasing marginal returns, it is as well possible that large setup costs and

economies of scale prevail in certain economies. In this case an uneven distribution of wealth

may be beneficial for growth as it lifts at least some people above the threshold to start a

profitable business or to obtain higher education, which could both exert positive spillover

effects.5

2.4 Endogenous Fertility

Initial inequality can be detrimental to growth due to a positive link between inequality and

the fertility rate. The transmission channel is closely related to the human capital argument

as decisions concerning human capital investment and family size are interrelated, according

to a prominent line of reasoning first brought forward by Becker and Barro (1988). In general,

most families are facing a trade-off between the quantity of children and their education.6

Poor parents may lack the resources to invest in the education of their children, particularly

when they are excluded from capital markets. Thus their only chance to increase family

income (or their old-age support) is to increase household size. In contrast, for richer parents

and in more developed countries, the opportunity costs of raising children are relatively high.

Thus it may be optimal to have fewer children and to invest more in human capital so that

they will earn higher lifetime incomes.7

Firstly, from this it follows, that poor societies tend to have high fertility rates and low

levels of education. Secondly, empirical evidence by Kremer and Chen (2002) underlines

that more inequality is associated with larger fertility differentials between educated and

uneducated women, foremost in middle-income countries.

Building up on this evidence, De la Croix and Doepke (2003) emphasize the growth ef-

fects of fertility differentials between the rich and the poor. In their model, income inequality

strictly stems from disparities in human capital provision. A mean-preserving spread in in-

come distribution increases the number of poorly educated children from underprivileged

families, relative to well-educated children from richer families. As the relative weight of

the less educated increases, average human capital in the aggregate economy is diluted and

economic growth in forthcoming periods is depressed. Additionally, due to the shape of the

fertility function proposed in the model, an increase in inequality is not only associated with

higher fertility differentials but also with a higher rate of overall fertility. Earlier growth re-

gressions conducted by Barro (2000) and Perotti (1996) have stressed the importance of the

aggregate fertility rate for understanding the transmission of inequality to economic growth.

5See Perotti (1993) for a model of a positive link between inequality, human capital investment, andgrowth based on this mechanism.

6It is often called the tradeoff bewteen the “quality” and the “quantity” of children.7See Galdor and Zang (1997), Morand (1999), Kremer and Chen (2002) and De la Croix and Doepke

(2003) for models of endogenous fertility arguing along this line of reasoning.

7

The work of De la Croix and Doepke (2003) confirms the empirical evidence provided by

Barro (2000), who finds that the aggregate fertility rate is negatively correlated with eco-

nomic growth and that the Gini coefficient of income inequality becomes insignificant when

the fertility rate is introduced into the regression model. Moreover, De la Croix and Doepke

(2003) provide evidence that differential fertility has a negative growth effect which even

remains significant if inequality and total fertility are held constant.

2.5 Political Economy: Market inequality and redistribution

Until now, we have described growth effects of inequality that are directly related to the

distribution of disposable income. However, there is another line of the literature emphasiz-

ing growth effects which are more closely related to market inequality and the redistributive

activity which may result from it. It was Perotti (1996) who called the theory put for-

ward by Bertola (1993), Alesina and Rodrik (1994) and Persson and Tabellini (1994) the

“endogenous fiscal policy approach”, dividing it into two successive arguments. The first

argument—called the political mechanism—is based on a seminal paper by Meltzer and

Richard (1981). It states that an unequal distribution of market income may create a high

demand for redistributive taxes and transfers via the political voting process. The second

argument—the economic mechanism—stresses negative incentive effects of redistribution.

By lowering the returns of investments in physical or human capital, redistributive taxes

may reduce capital accumulation and thus deter future growth.8 Moreover, a generous

welfare system may discourage labor effort.

The empirical literature provides little evidence for the channel of endogenous fiscal

policy. Particularly the results from Perotti (1996) are standing in sharp contrast to the

theoretical predictions.9 The data rejects the political mechanism as inequality is standing

in a negative relationship with redistribution, measured by the marginal tax rate and several

other measures of taxation and redistributive expenditures. Moreover, the results also con-

tradict the economic mechanism by showing that tax rates and various welfare expenditures

can be positively correlated with economic growth.

Milanovic (1999) suggests that the unexpected results from the earlier literature could

be due to the application of inadequate measures of inequality and redistribution. He

emphasizes that the endogenous fiscal policy channel is triggered by market inequality and

not by net inequality which was sometimes applied as an exogenous variable in earlier growth

regressions. Furthermore, he criticizes the use of imperfect measures of redistribution, as the

size of public transfers or taxes might say little about their redistributive impact. Using data

form the Luxembourg Income Survey (LIS), Milanovic calculates the distribution of market

8A first formalization of these incentive considerations was provided by Mirrlees, James (1971). Rebelo(1991) has focused on the effects of taxation in an endogenous growth model.

9Similar structural form estimates have been done before by Persson and Tabellini (1994) who have foundinconclusive results. Perotti (1994) studies the effects of endogenous fiscal policy regarding the investmentshare, but the results are inconsistent with the theoretical predictions.

8

income as well as effective redistribution, which is measured by the difference between the

distribution of market and disposable income. The data confirms the political mechanism.

Apparently, countries with a more unequal distribution tend to distribute more in favor of

poor households, but not necessarily in favor of the median voter.

Two additional arguments should be emphasized: Firstly, there is a further line of theo-

retical literature that considers the insurance effects of public redistribution. The existence

of a social safety net could be stimulating productive risk taking, entrepreneurship, and

innovation, which means that redistribution could be directly beneficial for growth.10 Sec-

ondly, it may make a difference whether the government redistributes directly through a

progressive tax system accompanied by transfer payments, or whether it provides indirect

redistribution via an improvement of equality of opportunities. While direct redistribution

may trigger negative effects on labor supply and the decision to invest in a child’s human

capital, indirect redistribution softens the budget constraint for poor families which leads to

an increase in both social mobility and the average level of human capital. A variety of en-

dogenous growth models illustrate the positive effects on incomes emanating from a higher

average level of human capital and the interweavement of human capital and innovation

activity.

3 Empirical strategy

Economic theory provides a manifold of theoretical channels through which inequality in-

teracts with growth. As some theories emphasize the positive economic effects of a more

equal society, others stress that the opposite is true. The question which strand of theory

dominates is—as often in macroeconomic explorations—an empirical one. In this section,

we empirically analyze the effect of inequality and redistribution on economic growth using

data for a large sample of countries between 1960 and 2014.

3.1 Empirical model and estimation technique

Our empirical model follows the specification of Barro (1991, 2003, 2013), considering the

growth rate of real per capita GDP a function

d

dt(y) = F (yt−1, ht,X,Ψ, R)

where yt−1 denotes initial GDP per capita, ht is human capital endowment per person,

and X comprises an array of control and environment variables. The application of the

latter is of great importance, as a number of authors, such as Mankiw et al. (1992) and

Barro (2003), conclude that the absolute convergence hypothesis proposed by the standard

growth model of Solow (1956), Koopmans (1965), and Cass (1965) cannot be confirmed

10Yet, Sinn (1996) shows that redistribution does not necessarily result in a decrease in net inequality.We will come back to this issue in our data description in section 3.

9

empirically. What is observable in the data, however, is conditional convergence, meaning

that the relation between the initial level of GDP per capita and the growth rate must be

examined after holding constant some crucial variables that distinguish the countries. The

variables include state and environment determinants and capture country specific potentials

for economic growth. When holding constant the effects emanating from these variables, we

study to what extent inequality Ψ and redistribution R can contribute to the explanation

of empirical growth patterns. It is crucial to specify the basic system very accurately, as the

disregard of covariates would lead to inconsistency in the estimated coefficients. Omitted

variables furthermore can yield an over-estimation of the coefficients of redistribution and

inequality, as these variables itself may depend on the political and institutional environment

of the countries. Moreover, empirical growth regressions often tend to be fragile to changes

in the specification. However, it is impossible to establish an empirical system that captures

all the growth determinants that have been identified by empirical research. Durlauf et al.

(2005) illustrate that there exist approximately as many growth determinants in empirical

literature as there are countries to be examined. It is thus difficult to identify the subset

that really matters. For this reason, we choose to apply the basic system specification of

Barro (1991, 2003, 2013) which has been proven to explain empirical growth patterns quite

accurately in a number of studies. As many of these variables can be considered channels

through which inequality assumes its influence on growth, we compare the outcome with

the results of reduced models consisting only of some of the variables in X.

In general, when taking the standard growth model serious, such a set of growth determi-

nants should also include physical capital in order to capture the effect of convergence when

economies approximate their individual steady state level. Yet data on physical capital is

quite unreliable due to arbitrary assumptions on depreciation and approximation on both

initial values and investment flows that need to be made for its calculation. This problem

can yield serious biases in the estimations, especially when the data set includes developing

economies. Thus, we assume that higher levels of yt−1 and ht reflect a greater stock of phys-

ical capital. As a result, an equiproportionate increase in yt−1 and ht reduces growth in the

basic specification, since the accumulation of reproducible factors inherently yields diminish-

ing returns. Human capital is approximated using average years of schooling (SCHOOLY).

A further dimension of human capital is health, as measured by life expectancy at birth

(LIFEEX). Health increases productivity since healthier people can work longer and harder.

Yet whereas life expectancies reflect better health, increasing rates of life expectancy simul-

taneously lead to an increase in effective depreciation. In order to disentangle these two

effects, we also include the logarithmic value of the fertility rate (FERT) in the empirical

system. By this means, we capture the negative growth effect of an increase in the popula-

tion, as the standard growth model predicts an increase in the effective depreciation rate if

fertility rates are higher, thereby reducing the steady state level of income. The second part

of the Solow model‘s central equation is captured by the investment share (INVS). In order

to isolate the effect of investment on growth rather than the reverse, we include the lagged

10

value of the investment share in the list of instruments. As poorer countries tend to have

lower investment shares, it is crucial to sort out the direction of causation between INVS

and the growth rate. A number of empirical studies, such as Alesina and Perotti (1996)

and Alesina et al. (1996) stress the importance of political stability in the growth process.

Especially when inequality is high, it can be expected that stability decreases; this, how-

ever, emanates harmful effects on investments and growth. Likewise, contractual freedom,

individual liberty, property rights and the duty to take reliability for own actions assume

direct effect on well-being, aside from the inequality growth nexus. To capture the effect of

political stability, we include the variable POLRIGHTS which gives the extent of political

liberty in a country.

Our analysis will also include government consumption (GOVC) which decreases the

steady state level of output per effective worker. This is because many consumption ex-

penditures of the government do not increase productivity, but are instead likely to cause

distortions in the private sector. Finally, we account for the extent of foreign trade relations

by incorporating the degree of openness (OPEN) as well as the terms of trade (TOTR)

into the system. Endogenous growth theory emphasizes the role of technological spillovers

in the growth process. Spillovers and technological diffusion increase the potential of im-

provements and vertical innovations, and enhance the level of knowledge of an economy. In

addition, trade directly enhances the cardinality of the set of capital goods available to be

utilized in the production. These mechanisms are the main trigger of growth in the sem-

inal endogenous models of Romer (1990), Aghion and Howitt (1992), and Grossman and

Helpman (1991). In adition, improvements in the terms of trade gauged by the ratio of

export prices to import prices are assumed to increase the real income of countries and thus

leads to a rise in domestic consumption. Furthermore, Diewart and Morrison (1986) note

that improvements in terms of trade may further have direct effects on GDP that resemble

technological progress, as such gains facilitate net output increases for any given amount of

domestic input factors. Furthermore, we include the inflation rate INFL in the empirical

model as a proxy of economic uncertainty. In combination with POLRIGHTS, the inflation

rate captures the effect of political instability.

Controlling for the variables described above, we wish to examine whether inequality

as measured by the Gini coefficient (GINI) and the amount of redistribution (REDIST)

affect the growth rate of per capita incomes. Such an investigation long has been strongly

restricted by data availability, as data concerning the Gini coefficient, and more importantly

the amount of redistribution has been on hand for only few countries and time periods. Yet

the Solt (2009) standardization of the WIID database dramatically increases the number of

degrees of freedom in empirical growth regressions that consider inequality, thereby enabling

the utilization of more elaborate estimation techniques.

We estimate the empirical model using the estimator proposed by Blundell and Bond

(1998). When working with macroeconomic panel data, unobserved heterogeneity often

turns out to be a serious problems. A simple way to overcome these problem would be a

11

one-way fixed effects model or a first difference approach such as Anderson and Hsiao (1982).

Yet, while the former most often suffers from a Nickell (1981) bias, the latter still has its

drawbacks in not exploiting all possible information available in the sample. Particularly,

the first-difference transform magnifies gaps in unbalanced panels. Thus, GMM may yield

more efficient estimates. One widely-used approach in the GMM context is the estimator

proposed by Arellano and Bond (1991). A potential weakness of this estimator is that

lagged levels are often poor instruments for first-differenced variables. The modifications of

Arellano and Bover (1995) and Blundell and Bond (1998) therefore includes lagged levels

as well as lagged differences. This estimator is often called the “System-GMM estimator”.

Blundell and Bond (1998) and Blundell et al. (2000) provide Monte Carlo evidence that

System-GMM is more robust than other dynamic panel data approaches in the presence

of highly persistent series. One concern that is inherent to all GMM estimators used in

growth regressions is the fact that GMM only is advantageous for large N and small T .

Yet, restricted data availability may lead to an insufficiently large number of cross-sections.

Soto (2009) analyses through Monte Carlo Simulations the performance of several standard

panel estimators if N is small (N = 100, 50, 35). The study concludes that the System-GMM

estimator yields the result with the lowest bias, declaring it to date as the most appropriate

method to investigate empirical growth patterns in the class of the standard dynamic panel

data estimators.

Our estimation strategy uses 5-year averages of the variables described above. This

approach is determined by the long-term perspective of growth regressions, the need to

smooth short-term fluctuations, and the availability of data. Estimating the origins of

economic growth using annual panel data would lead to severe biases and contradict the

implications of growth theory. In order to study whether other approaches yield different

results, we contrast our baseline findings to the outcome of Simultaneous Equation Models

utilizing 3SLS and GMM with HAC standard errors. The former equals the Barro (2000,

2003, 2013) approach.

A basic dynamic OLS model of the data described above would be

yit = θyit−1 + λhit + γΨit + δRit + βXit + ηi + ξt + vit (1)

where yit is the log of initial per capita GDP in i at 5-year period t, hit is human

capital endowment, ηi denotes country-specific effects, ξt is a time effect of period t, and

vit ≡ uit− ξt−ηi is the error term of the estimation. Neglecting unobservable heterogeneity

of the cross-section and period-specific effects, the error term would simply be uit, where

we would expect inconsistency and endogeneity. The marginal effects of our variables of

interest—inequality and redistribution—are captured in the coefficients γ and δ. The control

variables are included in Xit where β comprises the marginal effects.

To account for both a possible omitted variables bias due to unobserved heterogeneity,

and endogeneity, one could apply the Arellano-Bond approach, as has been done in empirical

12

growth regressions by, inter alia, Forbes (2000) and Panizza (2002). Define for reasons of

lucidity ∆k ≡ (kit − kit−1) and ∆2k ≡ (kit−1 − kit−2), the basic idea of this approach is to

adjust (1) to

∆y = θ∆2y + λ∆h+ γ∆Ψ + δ∆R+ β∆X + ∆ξ + ∆v (2)

and then using sufficiently lagged values of yit, hit, Ψit, Rit, and Xit as instruments

for the first-differences. However, as any fixed-effects attempt, this approach discards much

of the information in the data. In fact, it has been emphasized by a number of studies,

such as Barro (2000), Dollar and Kraay (2002), and Voitchovsky (2005) that inequality

time series are highly persistent. Thus, most of the variation in the data stems from cross-

sectional information. The Blundell-Bond estimator ensures that some of the information in

the equation in levels can be retained. We mentioned previously the superior properties of

System-GMM. As it is of great importance to exploit the remaining information as efficiently

as possible, the Blundell-Bond estimator reduces the relatively high cost of controlling for

unobservable heterogeneity, thereby providing less biased and more precise estimates than

Arellano-Bond.

To give a brief overview of the idea behind System-GMM, the estimator is computed

using moment conditions for equation (2) applying suitably lagged values as instruments

and additional moment conditions for the level equation in (1), utilizing lagged values of ∆k

and ∆2k as instruments. It is obvious that such an approach requires the condition that

Cov(∆k, ηi) = Cov(∆2k, ηi) = 0. A more elaborate description of the estimator especially

in the context of the empirical application can be found in Bond et al. (2001) and Roodman

(2009).

One further issue researchers are confronted with when estimating the empirical deter-

minants of growth patterns is the sorting out of the direction of causation. In a perfect

econometric world, the effect of governmental and individual behavior is isolatable and the

causality runs from the regressors to the dependent variable. In practice, however, economic

behavior of both institutions and households reflect reactions to economic events. If the

system model is incomplete, then it most likely does not hold that the covariates are un-

correlated with the error term, i.e. Cov(u,X) 6= 0. In such an event, endogeneity yields

inconsistent estimates of the coefficients. The application of lagged instrumental variables,

the consideration of unobserved heterogeneity, and the allowance of both heteroskedasticy

and autocorrelation within country errors helps to reduce these problems to a minimum

when applying Blundell and Bond (1998).

3.2 Data description

Our main explanatory variables are the Gini coefficient, which pictures overall personal in-

come inequality in each country, and the amount of redistribution. Since the theoretical

channels described previously are mostly related to the distribution of disposable income,

13

we focus on the Gini of income net of taxes and transfers in the baseline regression and

later turn to the effect of market inequality. We receive both variables from the Standard-

ized World Income Inequality Database (SWIID) generated by Solt (2009). The SWIID

is based on the UN World Income Inequality Database (WIID), which is the successor of

the Deininger and Squire (1996) data set. Both the WIID and the Deininger and Squire

databases have been applied in a number of studies, as both data sets at their time repre-

sented the most extensive collection of cross-country inequality data. Even so, panel data

analyses have long been strongly restricted due to the limited availability of data across

countries and over time. In addition, the original data lacks easy cross country and in-

tertemporal comparability. Atkinson and Brandolini (2001) provide an intense discussion of

this issue. The standardization of Solt (2009) tries to solve this problem while still offering

the widest possible data coverage. Indeed, the data provided by SWIID—especially since

their last update on September 2013—allows to evaluate the impact of inequality on growth

much more profoundly than most of the recent studies that often have to struggle with a

low number of degrees of freedom. The approach of Solt (2009) transforms and adjusts the

WIID in several steps, which are summarized in appendix A1.

Our standard redistribution variable is calculated as the difference between the market

Gini and the net Gini, whenever both are available in the SWIID. While we thus receive a

large sample of worldwide data, the resulting measure of redistribution has to be interpreted

with caution. Some of the data reflects only estimates stemming from other countries and

may thus insufficiently reflect information about public redistribution.11 Fortunately, from

version 3.0 onwards, the SWIID reports a subsample of more reliable redistribution data.

This redistribution variable is solely based on observations for which survey data on net and

gross incomes is available. The utilization of this measure ensures that redistribution is not

solely appraised using gross or net Ginis that were, themselves, calculated via some estimate

for redistribution. Furthermore, this variable strikes out the least reliable observations

coming from developing countries before 1985 and advanced economies before 1975. In

order to distinguish between the two measures in the empirical investigations, we denote the

full-sample of redistribution with REDIST, whereas the subsample of more convincing data

is named REDIST(S). As a matter of fact, when utilizing these variables, there is always a

trade-off between accuracy in the estimation due to a large number of degrees of freedom,

and accuracy in the measurement of redistribution.

The measurement of redistribution as the difference between market and net inequality

is fairly new in the empirical growth literature but common in the sociological and public

11Solt writes on his homepage: “Observations for redistribution are omitted for countries for which thesource data do not include multiple observations of either net or gross income inequality: in such cases,although the two inequality series each still constitute the most comparable available estimates, the differencebetween them reflects only information from other countries, and treating it as meaningful independentinformation about redistribution would be unwise. Similarly, because the underlying data is often thin inthe early years included in the SWIID, redistribution is only reported after 1975 for most of the advancedcountries and only after 1985 for most countries in the developing world.”

14

policy literature, where it is often named the pre-post approach.12 It covers the effective

redistribution through taxes and transfers, whereby several studies indicate that on average

most of the redistribution is achieved by transfers.13 However, redistribution via the in-kind

provision of public goods is not included, since it is not captured in the net Gini provided

in the SWIID. Like most inequality databases, the SWIID is based on surveys covering

household disposable income, not adjusted for the individual consumption of public good.

Furthermore, the pre-post approach does not cover public attempts to equalize market

inequality, neither by the promotion of equal opportunities nor by the interference of the

government in private wage agreements.

The pre-post approach comes with some further drawbacks, discussed in Bergh (2005).

One major problem is that market inequality is not independent from the extent of public

redistribution: On the lower end of the income scale, a generous welfare system may boost

gross inequality by encouraging poor people to withdraw from the labor market and to live

from transfers instead of market income. On the upper end, high income earners may be

discouraged by taxes and thus reduce their labor supply, which equalizes gross inequality. In

this paper, we follow Berg et al. (2014) who suggest that the effects on gross inequality can

be neglected as the effects on the lower and on the upper scale of the income distribution

are offsetting.

Whereas the pre-post approach measures the difference between the Gini coefficient of

income inequality before and after taxes, the more narrow redistribution variable REDIST(S)

proposed by Solt (2009) quantifies the estimated percentage reduction in market income

inequality due to taxes and transfers, which is defined as

GINI(M)−GINI(N)

GINI(M)∗ 100.

We calculate this data back in percentage point differences between net and market

Ginis in order to facilitate the interpretation and the comparability to the results based on

REDIST.

How is redistribution distributed across the countries available in the SWIID? Figure 1

illustrates the histogram of the variable using 5-year averages as in our empirical specifi-

cation. The mean value of redistribution in the sample is 4.92, indicating that on average

slightly less than 5 percentage points of the Gini coefficient are redistributed. Yet the vari-

ation around the mean is high as the standard deviation assumes a value of 5.51. The most

expansive social system redistributes 35.89, while some policies yield increases in inequality

(minimum value: -21.37). The Jarque-Bera test rejects the assumption of a normal distri-

bution. The distribution further is right-skewed (skewness: 0.93), illustrating that in most

12Van den Bosch, Karel and Cantillon (2008) provide an overview on the role of the pre-post approachin measuring the redistributive impact of taxes and transfers in public policy research. Berg et al. (2014)and Thewissen (2013) are to our knowledge the only studies so far which apply pre-post redistribution asan explanatory variable in growth regressions.

13See e.g. Joumard, Isabelle and Pisu, Mauro and Bloch, Debbie (2012) and Woo et al. (2013). The latteralso provide a comprehensive survey on the redistributive impacts of fiscal policy.

15

0

50

100

150

200

250

300

350

400

-20 -10 0 10 20 30

Series: REDIST

Observations 1126

Mean 4.921915

Median 3.341412

Maximum 35.89836

Minimum -21.37346

Std. Dev. 5.510409

Skewness 0.929855

Kurtosis 5.909100

Jarque-Bera 559.3118

Probability 0.000000

Figure 1 The distribution of the amount of redistribution across countries and over time, REDIST

economies, redistribution policies lead to a decrease of inequality.

When exploring the amount of redistribution, one can suspect that there are significant

differences between countries in different stages of development. This is indeed the case.

Using the classification of the World Bank, the mean value of redistribution in the sample

of high-income countries is 5.32 percentage points and substantially exceeds the mean re-

distribution in low-income countries (3.54). The higher developed an economy, the higher is

the average value of redistribution. The data also indicate major differences in distribution

policies across different political systems: In democracies, the mean value of redistribution

equals 6.15, whereas non-democratic forms of government tend to significantly redistribute

less (2.81).

The picture changes slightly when only considering REDIST(S) as illustrated in figure 2.

Both the mean and the median increase, indicating that the countries for which measures

of REDIST(S) are available on average tend to redistribute more. Yet, the number of avail-

able observation reduces from 1,126 in REDIST to only 517 when considering REDIST(S).

Furthermore, it is remarkable that the relative frequency of a high amount of redistribution

is substantially higher than in REDIST. At the same time, there are only few observations

where redistributive activities yield negative effects on the Gini coefficient. Most of the

differences towards the full sample arise from the larger share of developed economies in the

restricted sample.

The rest of the data that concerns our dependent and control variables stems from

commonly used data sets. The growth rate of real per capita GDP as well as the initial

level of GDP, the investment share (INV), the degree of openness (OPEN), and government

consumption (GOVC) are from PWT 8.0 as published by Feenstra et al. (2013). The

average years of schooling (SCHOOLY) is from Barro and Lee (2013) and includes the years

16

0

20

40

60

80

100

120

140

160

-5 0 5 10 15 20 25 30 35

Series: REDIST(S)

Observations 517

Mean 6.632946

Median 4.668673

Maximum 35.19725

Minimum -7.241432

Std. Dev. 5.858860

Skewness 0.912676

Kurtosis 3.718510

Jarque-Bera 82.89591

Probability 0.000000

Figure 2 The distribution of the amount of redistribution across countries and over time, RE-DIST(S)

of primary, secondary, and tertiary education that individuals of age 25 and older have

received during their educational training. Democracy and political liberty (POLRIGHT)

enters in the specification using data on political rights and liberty from Freedom House

(2014), which provides an index on democracy and rule of law where the data is d ∈ (1, 7).

As the variable is coded inversely, i.e. lower numbers are associated with higher rates of

democracy, we recode the variable applying POLRIGHT = 8− d in order to make sure that

the coefficient in the estimation illustrates the impact of an increase in democracy on growth,

rather than the reverse. The advantage of Freedom House (2014) is the large number of

available data in both time and cross-section, while the application of comparable indices

such as Jodice and Taylor (1983), Gupta (1990), Alesina and Perotti (1996), World Bank

(2013), and Vanhanen (2000) leads to a significant reduction of observations, especially in

the time dimension. We further use fertility rates (FERT), changes in the terms of trade

(TOTR), inflation rates (INFL) and data on life expectancy at birth (LIFEEX) as reported

by World Bank (2014).

Table 1 provides an overview of the data used in our empirical specification, their means,

maxima, minima, and standard deviations. Note that due to some periods of hyperinflation

that can be detected in the sample, the mean value of INFL is relatively high (6.67). The

high inflation observations took place in the 1990-1995 period in Congo and Georgia, and

during the 1985-1990 period in Bolivia. Further observations include Armenia, Angola,

Brazil, and Peru, although to a lesser extent. Most of these data points will not enter in

the regression, as the intersections of data availability of the regressors do not include the

critical observations in most cases.

One crucial point that has to be noted when examining the influence of inequality and

17

Table 1 Descriptive statistics of variables used in the regression

Variable N Mean Std. Dev. Min Max

GROWTH 1624 0.0218823 0.040689 -0.302555 0.3210244log(CGDP) 1626 8.387529 1.30292 5.317263 11.80222GINI(N) 1126 0.3890027 0.1114037 0.1605069 0.7643369GINI(M) 1126 0.4393836 0.0946583 0.185827 0.7561138REDIST 1126 0.0503809 0.0559007 -0.2137346 0.3589836REDIST(S) 517 16.77721 13.68035 -12.83622 50.61449INVS 1626 0.2064493 0.111355 -0.0131223 1.684564SCHOOLY 1584 5.900455 3.063374 0.04 13.09log(LIFEEX) 2027 4.126937 0.1997474 3.080882 4.42237GOVC 1626 0.2050443 0.1180274 -0.0240873 0.9337125INFL 1656 0.361469 2.624121 -0.0662845 69.62831OPEN 1822 0.7599636 0.4862227 0.0198891 4.378075POLRIGHT 1624 4.083528 2.194809 1 8log(FERT) 2029 1.2833 0.5502135 -0.1369659 2.21336TOTR 771 0.0700488 0.2676975 -0.6612992 0.9457714

redistribution on growth is that these variables cannot be analyzed in isolation. Instead,

the effects of the controls and the inequality variables are strongly interwoven and need to

be disentangled empirically, particularly as theory suggests that inequality does not directly

affect growth but assumes its influence through various transmission variables which often

are components of standard growth models. Indeed, there are some crucial correlations

between the covariates and the variables of interest, GINI(N), GINI(M) and REDIST. Table

A2 in the appendix depicts the correlation matrix of the data used in the regression.

As it turns out, the Gini coefficient net of taxes and transfers is correlated with a number

of variables used in the regression. Particularly the correlation with the fertility rate (65

percent ), the average years of schooling (-58 percent) and the amount of redistribution

(-60 percent) is high. The latter is obvious, as stronger redistribution policies directly

lead to a decline in inequality. Likewise, the higher the average human capital endowment

of individuals in a society, the lesser is the inequality of incomes. One interpretation of

this correlation concerns returns to education that may be lower in economies where the

average human capital level is higher, leading to a reduction in wage inequality. Indeed,

Psacharopoulus (1994) provides evidence that Mincerian returns to education decline when

average years of schooling increase. Furthermore, Barro (2000) and Perotti (1996) report

that greater inequality predicts significantly higher fertility rates. De la Croix and Doepke

(2003) argue that poor families —especially in unequal societies—tend to have a higher

fertility rate, so that education often cannot be provided to all of the children, thereby

leading to a decline in average school attainment.

The correlation matrix suggests that the latter channel indeed may be important, as high

fertility rates are associated with a lower number of average years of schooling (-64 percent).

If high fertility rates decrease social mobility via the education channel, then fertility and

18

inequality are positively correlated in the long-run.

A further relationship concerns the dependence of the investment share and the level

of inequality. A variety of theoretical channels—such as credit-market imperfections and

sociopolitical unrest–suggest that these variables should be negatively related. In fact, the

data exposes a negative correlation of -19 percent. However, the correlation significantly in-

creases when taking into account economies with less developed credit markets. In addition,

the correlation between life expectancy and inequality is strongly pronounced (-45 percent),

suggesting that higher rates of inequality lead to a decrease in life expectancy, as the poor

in strongly unequal countries do not have sufficient access to the health system, live in poor

hygienic conditions, or are undernourished. This may create a poverty trap, where a lower

health status on its part further enhances inequality.

The second focus of this paper is on the relation between inequality of market incomes,

redistribution and the resulting net inequality. It is therefore worthwhile to have a look at

the simple correlation between these variables before we use them as explanatory variables

in our growth regressions. Figure 3 pictures the level of net inequality compared to market

inequality in all countries for which reliable data is available in the 2005 to 2009 period. It

turns out that countries that are more unequal before redistribution are also more unequal

afterwards. Yet, the positive relationship that is obvious in the scatterplot is far from a

perfect correlation. Many observations are located considerably below a 45-degree line,

indicating a substantial amount of redistribution in several economies.14

Table 2 Regressions of REDIST(S) on market inequality

(1)POLS

(2)Fixed Effects

GINI(M) 0.135*[0.0712]

0.422***[0.0654]

log(CGDP) 0.0364***[0.00475]

-0.00369[0.00901]

Constant -0.323***[0.0541]

-0.0771[0.0671]

N 477 477R squared 0.366 0.407

Notes: The table reports regressions of REDIST(S) on GINI(M) using PooledOLS (column 1) and Fixed Effects (Column 2). Standard deviation is reportedin brackets.

According to the Meltzer-Richard effect, higher market inequality should lead to more

public redistribution. However, strong deviations from a positive relationship can be de-

tected in Figure 4, which shows public redistribution at different levels of market inequality.

14When we split the data into advanced and developing economies according to the threshold of 12,746USD defined by the World Bank, we find that the correlation is 97 percent in the developing sample. In thegroup of advanced economies the correlation is much weaker (54 percent), but still highly significant.

19

.2.3

.4.5

.6G

ini o

f net

inco

me

.2 .3 .4 .5 .6Gini of market income

Figure 3 The relationship between Gini of net income (GINI(N)) and Gini of market income(GINI(M))

Notes: The figure plots observations for each country within the 2005-2009 period. Data is from the restrictedsample

−.0

50

.05

.1.1

5.2

Red

istr

ibut

ion

.3 .4 .5 .6 .7Gini of market income

Figure 4 The relationship between the level of redistribution (REDIST(S)) and Gini of marketincome (GINI(M))

Notes: The figure plots observations for each country within the 2005-2009 period. Data is from the restrictedsample

20

Among the pictured observations a group of developing and newly developed countries is

standing out due to high levels of market inequality but low redistribution. One major

outlier is South Africa which has a Gini coefficient of market incomes of 63 percent, but

redistribution is much less pronounced (4 percentage points). Likewise, the data shows that

a number of developing economies tends to significantly redistribute less than advanced

economies. It is therefore necessary to hold constant the developing level when examining

the relationship between GINI(M) and REDIST(S). Table 2 is concerned with this inves-

tigation, where a basic model is used to explain the level of redistribution. The results

reveal substantial differences in redistribution activity across different developing levels in

the POLS estimation. When analyzing the influence of development using the Fixed-Effects

estimator, this influence vanishes. This may be traced back to the elimination of unobserv-

able heterogeneity. In both models, a higher level of market inequality leads to an increase

in REDIST(S).

A final relationship of interest concerns the correlation between redistribution and gov-

ernment consumption. Several earlier empirical studies, for instance Barro (2000, 2003,

2013), found a significant negative relationship between government consumption and eco-

nomic growth. Since redistributive expenditures like social transfers are classified as govern-

ment consumption, one could expect a close correlation between redistribution and govern-

ment consumption variables and thus anticipate a negative growth effect of redistribution.

Yet the correlation between REDIST and GOVC is surprisingly small in our sample. The

correlation coefficient of 6 percent shows that a great amount of government expenditures

is far from being redistributive. Alternatively, a large part of redistribution could emerge

via the progressivity of taxes and transfers which is not necessarily related to the size of

government expenditures. Our following examination of the relationship between growth

and effective redistribution is thus definitely worth the effort.

4 Regression results

4.1 Baseline regressions

Table 3 shows the results of our linear baseline growth estimations, when the full sample

of available data from the SWIID is used. Our full regression sample covers a maximum of

962 observations from 152 countries over a time span of 52 years, between 1960 and 2012.

The first Column shows a very basic specification in which—aside from time dummies

and country fixed-effects—the lagged level of per capita income is the only control variable.

As mentioned previously, theory does not suggest a direct effect of inequality on growth,

but rather proposes several transmission channels by which inequality affects growth. This

channels include standard growth determinants like the accumulation of physical and human

capital, fertility rates, and political stability. Ignoring some of the usual controls is thus the

only way of seizing the full growth effect of income inequality, although it comes with the risk

21

of an omitted variable bias. Column 1 shows the results of this stripped down estimation.

Indeed, the point estimate of the net Gini is similar to the baseline result from Berg et al.

(2014). It is highly significant and suggests that an increase of the Gini by one percentage

point would lower the yearly GDP growth rate by roughly 0.17 percentage points on average.

Notably and in contrast to Berg et al. (2014), the coefficient of redistribution is negative

but insignificant.

Table 3 Baseline regressions, full sample, redistribution variable is REDIST

(1) (2) (3) (4) (5)

log(CGDP) -0.00241[0.00476]

-0.0150***[0.00409]

-0.0203***[0.00417]

-0.0219***[0.00482]

-0.0227***[0.00543]

GINI(N) -0.168***[0.0487]

-0.0672***[0.0250]

-0.0543***[0.0206]

-0.00793[0.0216]

-0.00775[0.0379]

REDIST -0.146[0.0915]

-0.0737[0.0565]

0.00962[0.0343]

0.0230[0.0421]

0.0128[0.0476]

INVS 0.137***[0.0244]

0.0873***[0.0274]

0.0700***[0.0248]

0.0352[0.0326]

SCHOOLY 0.00719***[0.00187]

0.00274*[0.00155]

0.000775[0.00157]

0.00299[0.00219]

log(LIFEEX) 0.0893***[0.0173]

0.0515**[0.0245]

0.0491**[0.0211]

GOVC -0.0445*[0.0237]

-0.0516**[0.0223]

-0.0592**[0.0298]

INFL -0.000420[0.000485]

0.000108[0.000440]

-0.0000930[0.000777]

OPEN 0.00728[0.00481]

0.00812*[0.00491]

0.00646[0.00494]

POLRIGHT -0.00146[0.00164]

-0.00238[0.00157]

-0.00376*[0.00207]

log(FERT) -0.0363***[0.00751]

-0.0302***[0.00966]

TOTR -0.000283[0.00820]

N 962 867 745 745 385Countries 152 126 125 125 120Hansen 0.0168 0.307 1 1 0.985AR1 0.00000546 0.0000157 0.0000759 0.0000810 0.0133AR2 0.831 0.838 0.893 0.972 0.863Instruments 88 114 184 209 134

Notes: The table reports Blundell-Bond estimations. Standard deviation is reported in brackets.Hansen gives the J-test for overidentifying restrictions, AR1 and AR2 report the results of theAR(n) test. Instruments illustrates the number of instruments utilized in the regression.

In Column 2 the investment share and average years of school attainment are introduced

into our model. Both variables are essential components of empirical growth models but

also part of the transmission process according to the theories of unequal opportunities and

credit-market imperfections, sociopolitical unrest, and Keynesian saving rates. Thus one

22

would expect a lower impact of the Gini in this model. Indeed, the estimator of the Gini

shrinks to -0.067, which is less than half of Column 1, although it remains significant at the

1 percent level.15 The newly introduced controls are both positive and highly significant,

which is in line with theory and previous empirical studies.

The estimator of the Gini shrinks only slightly, when we introduce a number of additional

controls in Column 3. While the log of life expectancy—our health variable—is positively

related to economic growth, government consumption seems to stand in a negative rela-

tionship. Inflation, the simple openness ratio and political rights are insignificant in our

sample.

In Column 4 we incorporate the fertility rate into the model. Fertility and population

growth determine the capital-labor ratio and thus the steady-state output in the neoclassical

growth model. Furthermore, raising many children may either detract resources away from

the production of goods (see Becker and Barro (1988)), or force children into work and

thereby deter school attainment and human capital investments.16 Thus it is not surprising

that the coefficient on the log of the fertility rate is negative and highly significant. In

addition, the theoretical models mentioned previously propose that fertility is endogenous

to income inequality. Indeed, the estimator of the Gini becomes completely insignificant

when fertility is held constant in Column 4, which is in line with Barro (2000). As our

sample composition does not change from Regression 3 to Regression 4, the disappearance

of the Gini effect could be evidence for the endogenous fertility channel. However, one also

has to be aware of reverse causality: If inequality is more the result than the source of

fertility, our preceding estimations of the Gini may be biased. We examine this issue more

in detail when we regress fertility on inequality in section 4.4.

When we finally add the growth rate of the terms of trade to our model (Column 5), the

coefficient of the Gini remains virtually unchanged. Yet, our sample shrinks considerably

to only 385 observations, which is most likely the reason why many of our control variables

are losing significance. Due to the risk of sample selection bias and the absence of any

strong relationship between inequality and the terms of trade, neither from theory nor from

the correlations in our sample, the terms of trade variable will be spared out in all further

regressions.17

Until now, we have focused on the Gini but devoted only little attention to redistribution,

which seems to be insignificant in every specification so far. In fact, regarding the direct

effects of redistribution we recommend to interpret the results given in Table 3 cautiously.

While the maximum number of available observations is utilized here, redistribution may be

measured imprecisely in certain cases. In Table 4, we thus apply REDIST(S); which is the

15By controlling for investment and schooling successively and separately, we find that both variables havea similar effect on the estimator of the Gini.

16The latter effect is controlled for by our variable of average schooling.17It shall be noted that the results reported subsequently do not change considerably when incorporating

TOTR. Yet, neglecting TOTR enables the exploration on the basis of a significantly higher number ofdegrees of freedom.

23

Table 4 Baseline regressions, full sample, redistribution variable is REDIST(S)

(1) (2) (3) (4) (5)

GINI(N) -0.209***[0.0479]

-0.0537[0.0414]

-0.0467[0.0413]

-0.0186[0.0350]

0.000230[0.0377]

REDIST(S) -0.104[0.0891]

-0.0468[0.0769]

0.0628[0.0588]

0.0197[0.0674]

0.0727*[0.0413]

N 477 454 411 411 234Countries 80 74 73 73 72Hansen 0.211 0.778 1 1 1AR1 0.000163 0.0000779 0.0000711 0.0000243 0.00258AR2 0.678 0.781 0.179 0.123 0.115Instruments 69 91 157 179 134

Notes: The table reports Blundell-Bond estimations. Standard deviation is reported in brackets. Hansengives the J-test for overidentifying restrictions, AR1 and AR2 report the results of the AR(n) test.Instruments illustrates the number of instruments utilized in the regression.

alternative redistribution variable directly reported in the SWIID. As it is measured much

more accurately, it is recommended by the authors of SWIID when the focus lies specifically

on redistribution. Aside from the redistribution variable, the rest of the specification in each

column exactly follows the specifications shown in the same column in Table 3. However,

our regression sample now shrinks to a maximum of 477 observations from 80 countries,

inducing the risk of a some sample selection bias which may stem from the smaller share of

poor economies in the restricted sample.

In the restricted sample, the coefficient of redistribution is always insignificant and

changes its sign from negative to positive when government consumption is held constant in

Column 3. The estimated impact of the Gini coefficient is stronger than in table 3, at least

in the very basic model. Even more than in the full sample, it becomes obvious how in-

equality assumes its influence via its transmission channels: When investment and schooling

are controlled for in Column 2, the estimator of Gini shrinks by almost three quarters from

-0.21 to -0.054 and loses significance. In Column 4—when the fertility rate is incorporated

as an additional covariate—it virtually disappears. The latter strongly resembles the result

from the full sample estimations.

What have we learned about the relationship between inequality, redistribution and

growth so far? As in many previous studies, our results suggest that an equable distribution

of income is on average beneficial for growth. However, we emphasize the transmission chan-

nels by which inequality affects growth. If these channels are held constant, the correlation

between inequality and growth gradually disappears. Regarding the effects of redistribution,

we do not find evidence for the equity efficiency trade off so far. Considering the baseline

model illustrated in Column 1 of Table 4, the results impliy that a government which choses

to equalize the income distribution by one percentage point may lower growth by roughly

0.1 percentage points via the direct effects of taxes and transfers. On the other hand growth

24

may on average be improved by 0.2 percentage points due to the resulting reduction in

net inequality. So far, the net effect of redistribution seems to be positive. However, a

premature conclusion of the redistributive effect may be naive, especially in the light of the

transmission channels illustrated in the theoretical section. We will thus examine the overall

effects of redistribution and the channels though wich this effect is assumed more in detail

in the subsequent sections.

4.2 Sensitivity analysis of the baseline results

We are convinced that our estimation technique is the most promising approach to analyze

the effect of redistribution on economic growth as it best fits the data and encompasses most

of the econometric issues that emerge in empirical growth regressions. Yet it is essential to

analyze whether the results are due to an underlying economic rule or to the estimation

approach itself. For this reason, we want to investigate the stability of the results when

altering the applied estimator.

One technique often utilized in empirical growth explorations is the estimation of simul-

taneous equation models (SEM). This approach has been applied by, inter alia, Barro (2003,

2013) and Berthold and Grundler (2015). The general idea is to model each 5-year average

period as a separate equation and then estimate the coefficients using optimal GMM, and—

to ensure comparability to Barro (2003, 2013)—3SLS. The advantage of this approach is

that it eliminates endogeneity that is caused by simultaneity, as can be expected in empirical

growth patterns.

Table 5 illustrates the results of the SEM regressions. The findings remarkably resemble

the Blundell-Bond outcomes. Our main focus is on the effects of inequality and redistribu-

tion, both remaining unaltered in the SEM specification. The Gini coefficient net of taxes

and transfers is negatively associated with economic growth, whereas redistribution does not

significantly influence income increases in neither direction. This result emerges regardless

of the applied estimator, suggesting a high stability of the baseline findings. Furthermore,

there is only one major deviation in the covariates: The political rights index in the SEM

specification indicates that democracy contributes negatively to economic growth. However,

it must be emphasized that we already hold constant some crucial channels through which

democracy assumes its influence on income increases, particularly a higher average level

of schooling and health (both correlations are close to 60 percent), and higher investment

shares (correlation: 30 percent). As POLRIGHT is strongly associated with the level of de-

velopment, it may capture to some extent the effect of convergence, thereby contributing to

a negative influence on growth. Aside from POLRIGHT, all the variables have the expected

sign.

25

Table 5 Sensitivity analysis of the results

(1)GMM

(2)3SLS

log(CGDP) -0.0124***[0.000802]

-0.0121***[0.00173]

GINI(N) -0.0273***[0.0071]

-0.0275*[0.0153]

REDIST -0.00912[0.0112]

-0.015[0.0304]

INVS 0.028741***[0.006994]

0.025495[0.016626]

SCHOOLY 0.0783**[0.031]

0.0753[0.0665]

LIFEEX 0.00161[0.0098]

-0.00316[0.0215]

GOVC -0.7903[0.7605]

-0.619[1.4427]

INFL -0.00115**[0.000527]

-0.00184[0.00177]

OPEN 0.4491***[0.1298]

0.3054[0.2355]

POLRIGHT -0.2172***[0.046]

-0.2226*[0.0828]

log(FERT) -2.3949***[0.2196]

-2.4813***[0.4537]

N 644 644Countries 118 118R Squared .09, .29, .35, .02, .15, .33,

.23 .09.09, .30, .37, .02, .10, .33,.20, .11

S.E. .026, .019, .022, .027,.029, .025, .027, .026

.026, .018, .021, .028,

.021, .025, .027, .025Hansen 0.324234

Notes: The table reports 3SLS and GMM estimations with HAC standard errors. Stan-dard deviation is reported in brackets. Hansen gives the J-test for overidentifying restric-tions.

4.3 Overall effects of public redistribution at different levels of mar-

ket inequality

In section 4.1 we illustrate the effects of redistribution when net inequality is held constant.

As a consequence, the coefficient of redistribution can be interpreted as the intrinsic effect of

redistributive taxes and transfers, while the effects of net inequality are observed separately.

Based on these regressions we assess the overall effects of redistribution by comparing the

coefficients of redistribution and the net Gini, which resembles the approach of Berg et al.

(2014).

This section is concerned with an alternative view on the inequality growth nexus. Sub-

26

sequently, we aim to directly examine the overall growth effects of public redistribution.

By leaving net inequality open, the coefficient of redistribution explicitly comprehends both

the direct incentive effect of redistributive taxes and transfers, and the indirect effect of the

resulting net inequality. Gross Ginis, which are possibly effected further by some feedback

effects of redistribution, can be held constant in this case, i.e. we are examining the overall

effects of redistribution for a given level of market inequality [GINI(M)].18 The results from

this setting are given in Table 6. In contrast to our baseline specification, the coefficient

of REDIST(S) now becomes significant and positive in the basic model given in Column 1,

but also in most of the other specifications. The estimator picks up the insignificant effects

of redistribution plus the significantly positive effect of a lower level of inequality, which we

have already seized in previous estimations. For a given level of market inequality public

redistribution seems to be beneficial for growth as long as all transmission channels of net

inequality are left open. Market inequality, however, is also negatively correlated to eco-

nomic growth. As no theory aside from the explanation arguing via redistribution explains

a negative effect of market inequality itself, we assume that market inequality is still captur-

ing parts of the effects of net inequality in these regressions. Despite of redistribution both

market and net Ginis are still closely correlated in the overall sample, as we have shown in

the data description of section 3.2.

In Column 2 the basic model is again augmented by the investment share and average

school attainment. As two of its main transmission channels are incorporated, the coeffi-

cients of redistribution and market Gini shrink considerably and become insignificant. Thus

the increase in the estimators of interest in the successive extended models is somewhat

surprising. While both GINI(M) and REDIST(S) turn out to be significant in Column 3,

the effect of redistribution remains significantly positive, even when we control for the fer-

tility rate in Column 4. Thus, redistribution seems to be contribute to growth even when

some crucial covariates—investments, schooling and fertility—remain unchanged. However,

one has to bear in mind that the regression models given in Columns 3 and 4 control for

government consumption. Government consumption is positively related to redistribution

and exerts a negative effect on growth. An increase in redistribution usually goes along

with rising government consumption, which is negative for growth and thus may offset the

originally positive effect found in table 6. However, when government consumption remains

constant, redistribution is growth enhancing, even when some of its transmission channels

are held constant. An interpretation of this result is that redistribution is most growth

friendly when it does not stem from the size of taxes and transfers (and thus the volume of

government consumption) but rather from its progressiveness.

So far we know that inequality is on average detrimental for growth and that public re-

distribution in sum seems to be growth enhancing. Subsequently, we aim to explore whether

the growth effect of redistribution depends on the initial level of market inequality. There is

18Ideally we would be examining the growth effects of market inequality, redistribution and net inequalityall at once. However, this is of course not possible due to perfect multi-collinearity.

27

Table 6 Overall effects of redistribution

(1) (2) (3) (4)

log(CGDP) -0.0191***[0.00681]

-0.0195***[0.00434]

-0.0308***[0.00835]

-0.0364***[0.00768]

GINI(M) -0.211***[0.0494]

-0.0468[0.0390]

-0.0612*[0.0358]

-0.0495[0.0477]

REDIST(S) 0.144*[0.0853]

0.00714[0.0630]

0.113**[0.0530]

0.0996*[0.0581]

INVS 0.173***[0.0312]

0.113***[0.0308]

0.0890***[0.0258]

SCHOOLY 0.00677***[0.00201]

0.00680***[0.00212]

0.00445**[0.00191]

log(LIFEEX) 0.0745[0.0544]

0.0720[0.0479]

GOVC -0.0994***[0.0259]

-0.117***[0.0249]

INFL -0.000370[0.000894]

-0.0000295[0.000786]

OPEN 0.00789[0.00643]

0.00346[0.00462]

POLRIGHT -0.00107[0.00287]

-0.00126[0.00161]

log(FERT) -0.0405***[0.0101]

N 477 454 411 411Countries 80 74 73 73Hansen 0.192 0.777 1 1AR1 0.000166 0.0000798 0.0000627 0.0000228AR2 0.717 0.748 0.222 0.182Instruments 69 91 157 179

Notes: The table reports Blundell-Bond estimations. Standard deviation is reported in brackets.Hansen gives the J-test for overidentifying restrictions, AR1 and AR2 report the results of theAR(n) test. Instruments illustrates the number of instruments utilized in the regression.

reason to suspect that there may remain an undetected negative effect of redistribution, as

suggested by the negative but insignificant sign of redistribution in the baseline regressions.

If true, the negative effect of redistribution on incentives has to be be balanced with the

positive effects of an equable income distribution; possibly arguing for more redistribution

when market inequality is high and less when it is low. We test this conditional effect of

redistribution by introducing an interaction term between redistribution and the market

Gini into our model. The results given in Table 7 offer tentative evidence as the interaction

term is positive and significant in the restricted models illustrated in Columns 1 and 2,

while redistribution is now significantly negative. When calculating the marginal effects of

redistribution based on the results of Column 1, we learn that redistribution acts negatively

as long as the market Gini is lower than 43, which is its median value in our sample, but

becomes positive afterwards. Given a market Gini of 37–which is the 25th percentile of our

28

Table 7 Interaction of redistribution with market Gini

(1) (2) (3) (4)

Log(CGDP) -0.0108[0.00785]

-0.0137***[0.00495]

-0.0278***[0.00674]

-0.0301***[0.00648]

GINI(M) -0.275***[0.0514]

-0.116**[0.0454]

-0.122**[0.0563]

-0.0561[0.0620]

REDIST(S) -0.542*[0.314]

-0.548**[0.269]

-0.118[0.205]

-0.100[0.185]

GINI(M)×REDIST(S) 1.275**[0.513]

1.101**[0.476]

0.493[0.415]

0.364[0.379]

INVS 0.153***[0.0301]

0.113***[0.0371]

0.0925***[0.0255]

SCHOOLY 0.00598***[0.00200]

0.00564***[0.00213]

0.00355**[0.00166]

log(LIFEEX) 0.0732[0.0551]

0.0396[0.0462]

GOVC -0.0967***[0.0286]

-0.115***[0.0246]

INFL -0.000363[0.000955]

-0.000325[0.000851]

OPEN 0.00219[0.00693]

0.00175[0.00503]

POLRIGHT -0.000900[0.00237]

-0.00134[0.00148]

log(FERT) -0.0429***[0.00985]

N 477 454 411 411Countries 80 74 73 73Hansen 0.591 0.976 1 1AR1 0.000233 0.000185 0.000116 0.0000345AR2 0.881 0.713 0.262 0.170Instruments 86 108 196 196

Notes: The table reports Blundell-Bond estimations. Standard deviation is reported in brackets.Hansen gives the J-test for overidentifying restrictions, AR1 and AR2 report the results of theAR(n) test. Instruments illustrates the number of instruments utilized in the regression.

sample— equalizing the Gini by one percentage point would lower the growth rate by 0.07

percentage points. At a market Gini of 47—the 75th percentile—growth would on average

be increased by 0.06 percentage points. The results of this more in-depth analysis is crucial

for policy implications, as it suggest that the initial level of inequality assumes a significant

impact on the direction of the influence of redistribution.

Before closing our study on the growth effects of redistribution we want to recall the po-

tential feedback effects of redistribution on market inequality which we only briefly addressed

in section 3. So far we have considered two options to study the impacts of redistribution:

In the first option, when the net Gini is controlled for and the gross Gini is left open, redis-

tribution features only the direct effects of taxes and transfers, but inherits feedback effects

29

on gross inequality. In the second option redistribution explicitly comprehends its indirect

effects via net inequality, but market inequality is held constant.

4.4 Empirical investigation of the transmission channels

The previous regressions suggest that inequality may be harmful for growth. However,

as illustrated in the theoretical section, inequality assumes its influence via a number of

transmission channels. Studying the underlying causes of the negative effect of inequality

provides a more in-depth understanding of the relationship between the extent of inequality

in societies and their level of income.

According to the theoretical literature reviewed previously, income inequality should

be particularly detrimental to growth when credit is not available to equalize investment

and education opportunities. Empirically, such a conditional effect can be detected by the

introduction of an interaction term. Ideally, the Gini would be interacted with a moderator

variable that directly measures the ease of financing profitable investments or education for

poorer people. As such a variable is not available, we use the ratio of private credit to GDP

(CREDIT) as a proxy.19

In the very basic model given in Column 1 of Table 8 both the Gini and its product with

CREDIT are highly significant, individually and jointly. The coefficients imply that the

marginal effect of the Gini is negative at low values of CREDIT but becomes positive at a

credit to GDP ratio of roughly 0.8, which is located around the 80th percentile of our sample.

Thus we find evidence for a negative growth effect of inequality in case of credit market

imperfections. Moreover, inequality seems less severe in countries where financial markets

are highly developed. According to these findings, we can assume that much of the negative

influence of inequality on growth is due to forgone investments in human and physical capital.

Especially highly productive investment opportunities may fall by the wayside. In countries

where the possibilities of these investments are insufficient and where individuals at the

bottom of the income distribution are hindered from exploiting their full intellectual and

entrepreneurial potential, growth rates are lower. Unfortunately, we can only control for the

quantity of both transmissions variables, but not for their average productivity, which should

be primarily affected by inequality. This is one reason why the estimator of the interaction

term becomes smaller, but still remains significant when we include the investment share and

the average years of school attainment in Column 2.20 Similarly to the baseline regressions,

the Gini and its product with CREDIT only becomes insignificant if the fertility rate is held

constant in Column 4. This is in line with theoretical models predicting that human capital

investments are related to fertility decisions. If poorer families gain access to credit they

may prefer to invest in human capital and to reduce fertility. By incorporating the fertility

rate in the model, we effectively eliminate this transmission channel so that the effect of

19We instrument the credit ratio and the interaction term with its lagged values, as it is possibly endoge-nous to growth. The data source of CREDIT is World Bank (2014).

20A Wald test of the joint significance of Gini and the interaction term yields a p-value of 0.057.

30

Table 8 Interaction between the Gini coefficient, credit, and public spendings on education

(1) (2) (3) (4) (5) (6)

log(CGDP) 0.00176[0.00417]

-0.00579[0.00476]

-0.0147***[0.00345]

-0.0192***[0.00329]

-0.00202[0.00411]

-0.271***[0.0741]

GINI(N) -0.189***[0.0454]

-0.0732**[0.0325]

-0.0665**[0.0306]

-0.00796[0.0357]

-0.271***[0.0741]

-0.160***[0.0485]

CREDIT -0.0944***[0.0193]

-0.0514***[0.0156]

-0.0321**[0.0143]

-0.00358[0.0161]

PSE -1.471**[0.634]

-1.284***[0.495]

GINI×CREDIT 0.235***[0.0565]

0.102**[0.0463]

0.0657*[0.0399]

-0.00378[0.0445]

GINI×PSE 2.746**[1.278]

2.178**[1.024]

REDIST -0.0760[0.0609]

-0.0147[0.0447]

0.0241[0.0423]

0.00983[0.0383]

-0.0955[0.0711]

-0.0292[0.0448]

INVS 0.125***[0.0215]

0.0894***[0.0296]

0.0731***[0.0252]

0.109***[0.0217]

SCHOOLY 0.00453**[0.00178]

0.00182[0.00134]

-0.0000431[0.00134]

0.00663***[0.00156]

log(LIFEEX) 0.0917***[0.0147]

0.0555***[0.0214]

GOVC -0.0280[0.0209]

-0.0384*[0.0231]

INFL -0.000713[0.000519]

-0.000396[0.000442]

OPENCO 0.00635[0.00451]

0.00749[0.00484]

POLRIGHT -0.00215[0.00183]

-0.00236*[0.00144]

log(FERT) -0.0369***[0.00863]

N 899 809 719 719 718 659Countries 149 124 123 123 143 123Hansen 0.278 0.988 1 1 0.309 0.924AR1 0.00000414 0.0000194 0.0000745 0.0000942 0.00000429 0.000000822AR2 0.766 0.893 0.886 0.928 0.882 0.911Instruments 140 166 234 259 124 149

Notes: The table reports Blundell-Bond estimations. Standard deviation is reported in brackets. Hansengives the J-test for overidentifying restrictions, AR1 and AR2 report the results of the AR(n) test. Instru-ments illustrates the number of instruments utilized in the regression.

inequality vanishes.

We can further investigate the transmission channels with the help of another condition-

ality discussed above. It is clear that a dissipation of intellectual potential will foremost

occur if inequality is high and education is expensive for poorer households. Thus the

growth effect of inequality could depend on the volume of public spending on education

31

(PSE), which can serve as a substitute for deficient private education spending.21 Indeed,

the interaction term between Gini and the ratio of public education spending to GDP is

positive and significant at the 5 percent level, both in the very basic model shown in Column

4 of Table 8, but also if the investment share and schooling are held constant in the second

regression. Mindful of the negative and highly significant impact of the Gini coefficient, it

turns out that the marginal effect of inequality is larger if public education expenditures

are low. If public education expenditures increase, the marginal effect of inequality becomes

smaller. However, the effect remains negative unless education expenditures are roughly

10 percent of GDP, which is an extremely high value, located in the 99th percentile of our

sample.22

In many of our reduced form regressions reported previously, the introduction of certain

control variables considerably changes the estimated coefficient of the Gini. There is much

reason to suspect that this is due to the transmission channels through which inequality

acts. If we hold constant these channels—e.g. if we control for fertility, investment, and

schooling—the impact of inequality becomes smaller and eventually insignificant. Subse-

quently, we aim to directly examine how inequality and redistribution affect the three most

common transmission variables, namely the investment share, schooling, and fertility. Sim-

ilar to Barro (2000) we regress each transmission variable on the empirical model which

we have used before to explain economic growth. While the model and estimation method

is not perfectly suited for explaining the transmission variables, this procedure comes with

the advantage of good comparability among the transmission regressions and with our main

growth regressions.23 The results from this exercise are shown in Table 9.

Regarding the estimation of the investment share given in Column 1, a significant im-

pact of net inequality seems to be nonexistent. As the positive effect of differential saving

rates is counteracted by the channels of capital market imperfections and sociopolitical un-

rest, which are closely related to investments into physical capital, this finding is broadly in

line with economic theory. Notably, investment is strongly negatively related to redistribu-

tion. Apparently, incentive effects play an important role here, which is not surprising as

redistributive taxes should be directly affecting the return of investment projects.

The estimations of schooling and fertility directly confirm our expectations from the

growth regressions. While average years of school attainment are negatively related to

inequality, the fertility rate is standing in a positive relation to the Gini coefficient, resem-

bling the results from Perotti (1996) and Barro (2000). Apart from its indirect effect via a

lower level of net inequality, redistribution is insignificantly related to schooling and fertility.

Nevertheless, one could speculate that the negative sign of redistribution in the schooling

21The data soucre of PSE is World Bank (2014).22In the augmented model given in Column 2 the critical value is located around 7 percent of GDP.

Interaction effects are robust to the exclusion of observations with extremely high and possibly doubtfulvalues of public education expenditure (> 10).

23Although the Blundell-Bond estimator is designed for dynamic models, the xtabond2 command doesnot necessarily require the dependent variable to appear on the right hand side, see Roodman (2009).

32

Table

9T

ransm

issi

on

channel

sof

ineq

uality

(1)

INV

S(2

)SC

HO

OLY

(3)

FE

RT

(4)

INV

S(5

)SC

HO

OLY

(6)

FE

RT

log(C

GD

)0.0

799***

[0.0

155]

0.8

06***

[0.2

86]

-0.4

46***

[0.1

39]

0.0

393**

[0.0

168]

1.4

89***

[0.2

89]

-0.5

87***

[0.1

37]

GIN

I(N

)-0

.0723

[0.1

43]

-10.8

7***

[2.6

37]

6.4

29***

[1.3

85]

-0.3

25**

[0.1

60]

-12.8

5***

[3.5

58]

9.6

81***

[2.0

58]

RE

DIS

T(S

)-0

.832**

[0.3

29]

-2,4

40

[3.1

68]

2,3

46

[1.9

24]

-0.4

89*

[0.2

70]

-7.0

04*

[3.8

00]

0.5

25

[2.3

68]

GIN

I(N

)×

CR

ED

IT0.4

25**

[0.2

11]

5.5

91*

[3.0

96]

-8.0

29***

[2.3

25]

CR

ED

IT-0

.0978

[0.0

695]

-2.9

09***

[1.0

34]

3.2

20***

[0.7

61]

N477

454

469

447

428

447

Countr

ies

80

74

79

78

73

78

Hanse

n0.2

11

0.3

53

0.0

959

0.9

72

0.9

98

0.9

92

AR

10.5

17

0.0

244

0.0

0000914

0.5

14

0.0

788

0.0

0141

AR

20.0

650

0.3

61

0.2

53

0.2

45

0.1

66

0.0

617

Inst

rum

ents

69

69

69

113

113

113

No

tes:

Th

eta

ble

rep

ort

sB

lun

del

l-B

on

des

tim

ati

on

s.S

tan

dard

dev

iati

on

isre

port

edin

bra

cket

s.H

an

sen

giv

esth

eJ-t

est

for

over

iden

tify

ing

rest

rict

ion

s,A

R1

an

dA

R2

rep

ort

the

resu

lts

of

the

AR

(n)

test

.In

stru

men

tsillu

stra

tes

the

nu

mb

erof

inst

rum

ents

uti

lize

din

the

regre

ssio

n.

33

regression hints a negative incentive effect of redistributive taxes and transfers.

From theory we know that capital market imperfections should be reinforcing the impact

of inequality on its transmission variables. Indeed, an interaction term between GINI and

CREDIT is significant in all three regressions. The negative effects of inequality—both on

investments and schooling—are stronger if availability of credit is low. Moreover, the positive

correlation between inequality and fertility is strongest if credit availability is limited. Thus

the mutual relation between inequality, schooling and fertility becomes obvious. Our data

seems to confirm the endogenous fertility channel. Facing the trade-off between the quantity

and education of their children, poorer families seem to choose quantity instead of education

when inequality and credit market imperfections are high. As De la Croix and Doepke

(2003) illustrate, this can lead to the decline in economic growth. This is exactly what we

observe in our growth regressions. From a policy perspective our results argue for enhancing

the equality of opportunities by directly providing an equal access to education. Focusing

on redistribution instead may partly offset the positive effects of lower inequality, as the

significant coefficient of REDIST(S) in Column 5 suggests negative incentive effects of larger

taxes and transfers.

4.5 Different development levels

The basic regression results suggest that inequality and growth are negatively related. Yet,

we drew this conclusion on the basis of the whole sample, where we can suspect that there

are substantial differences between the countries, especially between countries of diverging

development levels. One early hypothesis of the relation between inequality and develop-

ment comes from Kuznets (1965) who proposes a parabolic link between the two variables.

The underlying mechanism of this relationship is structural change: In agricultural societies,

all individuals earn more or less comparable wages. Yet, the emergence of technical change

increases productivity of the workers that are able to master the new technologies. Eventu-

ally, a large fraction of individuals learns to handle the new technologies, but this is a process

which requires both educational training, and time. Short time after the introduction, only

few workers can be employed in the new sector. Marginal productivity compensation raises

wages in the new sector, redounding to a substantial increase in wage inequality. As more

and more individuals gain access to the new technologies and the required human capital

level, wage inequality eventually goes back to the initial level. Theoretically, the process

is completed when all individuals are employed in the new sector. More generally, the

skill-premium can be explained not only if some major structural changes appear, but also

if innovations or productivity-enhancing improvements are implemented in existing sectors.

Here we can use two different lines in the argumentation: First, as Aghion and Howitt (1998)

point out, the emergence and diffusion of new technologies triggers wage inequality across

skill groups. Yet a substantial amount of the increase in wage inequality over the past years

has been due to inequality within education groups. Therefore, the theory can be enlarged

34

to a second branch incorporating wage inequality within education groups. Aghion and

Howitt (2002) argue that most of this type of inequality is originated in learning-by-doing

and intersectoral mobility. However, when investigating the relationship between the two

variables using macroeconomic data, we can only explore the first effect, as there is hardly

any data concerning the second branch of argumentation.

Barro (2008) provides evidence that there are indeed some major deviations in the impact

of inequality on growth when analyzing different income levels. As it turns out, economies

with incomes less than 11,900 USD show a negative correlation between inequality and

growth, whereas the relationship is positive in the group of countries with yearly incomes

that increase the critical threshold of 11,900 USD. Barro (2008) investigates the period

1965-2004 where strong restrictions in data availability concerning inequality prompts him

to estimate the effect only on the basis of very few observations (depending on the regression

between 47 and 70 in the whole sample estimates). The larger availability of data in SWIID

and other sources allows us to explore this link on a broader basis. Especially with respect to

less-developed countries, the unavailability of inequality data for some countries may cause

a sample selection bias in studies building on earlier data. In addition, low-income countries

on average have gained some major income increases over the past ten years (+62 percent

between 2003 and 2013), offering the possibility that the distinct effect observed by Barro

(2008) may have vanished.

Table 10 illustrates the results of our baseline estimations, conducted separately for

high and low income countries. The critical value separating the two income groups is

12,746 USD, as proposed by the classification of the World Bank. The results obtained

from the separation of the sample according to the level of development yields unambiguous

conclusions: If only the countries with incomes less than 12,746 USD are to be examined,

there is a strong and significantly negative influence of inequality on growth. However, as the

economies develop, the growth-hindering effect of inequality declines and eventually becomes

insignificant. In the sample of high-income countries, the Gini coefficient even assumes a

positive impact on growth, albeit this influence is far from significance. With few exceptions,

the control variables of the regression remain relatively stable, even though there are some

deviations in the effect of life expectancy and government consumption. The former may be

due to the ambiguous effect emanating from the variable. Unlike in developing economies,

the depreciation-increasing effect of population growth may predominate the health-effect

in advanced economies, especially as the level of health does not deviate substantially in

high-income countries (standard deviation high-income: 5 years, low-income: 11 years).

Regarding government consumption, a possible explanation is that financial markets are

much more developed in high-income countries. The crowding out effect of government

consumption may thus be less severe than in low income countries. In addition, it can be

suspected that government spendings are used more efficiently in advanced economies, as a

higher level of factor productivity accompanied by less corruption and a more consequent

devotion to the quality of the education sector may exert positive effects on economic growth.

35

Table

10

The

impact

of

ineq

uality

on

gro

wth

for

diff

eren

tle

vel

sof

dev

elopm

ent,

base

line

regre

ssio

nfo

rhig

hand

low

inco

me

countr

ies

Low

-Inco

me

Countr

ies

Hig

h-I

nco

me

Countr

ies

(1)

(2)

(3)

(4)

(1)

(2)

(3)

(4)

log(C

GD

P)

0.0

125**

[0.0

0491]

-0.0

0955**

[0.0

0393]

-0.0

190***

[0.0

0518]

-0.0

219***

[0.0

0553]

-0.0

192**

[0.0

0748]

-0.0

182**

[0.0

0793]

-0.0

120

[0.0

169]

0.0

126

[0.0

162]

GIN

I(N

)-0

.133***

[0.0

487]

-0.0

905***

[0.0

230]

-0.0

400

[0.0

257]

-0.0

110

[0.0

262]

0.0

414

[0.0

862]

0.0

409

[0.0

954]

-0.0

484

[0.1

10]

0.0

624

[0.1

01]

RE

DIS

T0.0

102

[0.0

718]

0.0

162

[0.0

537]

0.0

726

[0.0

637]

0.0

664

[0.0

647]

-0.0

931

[0.0

755]

-0.0

555

[0.1

11]

-0.0

232

[0.1

43]

-0.0

513

[0.1

42]

INV

S0.1

41***

[0.0

241]

0.0

690**

[0.0

307]

0.0

507

[0.0

328]

0.0

909

[0.0

583]

0.0

373

[0.0

776]

0.1

14

[0.0

796]

SC

HO

OLY

0.0

0617***

[0.0

0166]

0.0

0231

[0.0

0163]

-0.0

00267

[0.0

0161]

0.0

000280

[0.0

0265]

-0.0

0137

[0.0

0274]

-0.0

00482

[0.0

0273]

log(L

IFE

EX

)0.0

824***

[0.0

188]

0.0

578**

[0.0

226]

-0.0

861

[0.1

04]

-0.2

09*

[0.1

07]

GO

VC

-0.0

360*

[0.0

215]

-0.0

451*

[0.0

245]

0.0

843

[0.1

94]

0.1

54**

[0.0

730]

INF

L-0

.000787

[0.0

00534]

-0.0

00150

[0.0

00407]

-0.0

121

[0.0

143]

0.0

0204

[0.0

135]

OP

EN

0.0

0789

[0.0

0685]

0.0

114*

[0.0

0663]

0.0

0481

[0.0

0919]

-0.0

00665

[0.0

0547]

PO

LR

IGH

T0.0

000833

[0.0

0182]

-0.0

00831

[0.0

0120]

0.0

00806

[0.0

0880]

0.0

0354

[0.0

0377]

log(F

ER

T)

-0.0

380***

[0.0

0893]

-0.0

299*

[0.0

158]

N674

586

504

504

259

253

220

220

Countr

ies

128

103

99

99

43

42

38

38

Hanse

n0.3

01

0.8

76

1,0

00

1,0

00

1,0

00

1,0

00

11

AR

10.0

00101

0.0

00175

0.0

00532

0.0

00613

0.0

0512

0.0

165

0.0

184

0.0

237

AR

20.6

05

0.5

90

0.9

56

0.9

28

0.4

83

0.6

58

0.7

53

0.9

91

Inst

rum

ents

88

114

184

209

88

114

183

203

No

tes:

Th

eta

ble

rep

ort

sB

lun

del

l-B

on

des

tim

ati

on

s.S

tan

dard

dev

iati

on

isre

port

edin

bra

cket

s.H

an

sen

giv

esth

eJ-t

est

for

over

iden

tify

ing

rest

rict

ion

s,A

R1

an

dA

R2

rep

ort

the

resu

lts

of

the

AR

(n)

test

.In

stru

men

tsillu

stra

tes

the

nu

mb

erof

inst

rum

ents

uti

lize

din

the

regre

ssio

n.

36

Table 11 The impact of inequality for different levels of development

(1) (2) (3) (4)

log(CGDP) -0.0371***[0.0118]

-0.0415***[0.00919]

-0.0164*[0.00921]

-0.0200**[0.00850]

GINI(N) -0.933***[0.253]

-0.683***[0.191]

0.0390[0.183]

0.0776[0.157]

GINI(N)×CGDP 0.0976***[0.0311]

0.0752***[0.0233]

-0.00839[0.0213]

-0.00853[0.0184]

N 962 867 745 745Countries 152 126 125 125Hansen 0.103 0.684 1,000 1,000AR1 0.00000664 0.0000163 0.0000827 0.0000828AR2 0.922 0.999 0.824 0.960Instruments 111 137 207 232

Notes: The table reports Blundell-Bond estimations. Standard deviation is reported in brackets.Hansen gives the J-test for overidentifying restrictions, AR1 and AR2 report the results of theAR(n) test. Instruments illustrates the number of instruments utilized in the regression.

In developing economies, contrariwise, some government expenditure may be used much less

efficiently, especially in the light of a possibly higher extent of nepotism, crony capitalism,

and higher rates of corruption.

Figure 5 pictures the marginal effect of the Gini coefficient for different development

levels and the 95 percent confidence interval. The underlying model is Coulmn 1 of Table

11, where the interaction between the Gini coefficient and the development level allows us

to investigate the impact of inequality as the economies evolve. We conduct the analysis

identically to the baseline specification; however, for reasons of lucidity, we only report the

inequality variables, as there are virtually no changes in the covariates. It turns out that

the marginal effect of inequality is remarkable and significantly negative in poor economies,

whereas the coefficient of inequality increases proportionately to ascending income levels.

At the same time, the significance of inequality decreases as the econmies develop. The

null is reached at an income level of 14,130 USD, which resembles the critical threshold of

high-income groups that we assessed in the sample separation. The coefficient of inequality

continues to increase in the group of rich countries, whereas the positive effect does not

become significant, even at the rear edge of the function pictured in figure 5. However,

the running of the function implies that there may be a possibility that the marginal effect

continues to increase and eventually will be significant in economies that exceed the current

level of development, but such considerations are pure speculation.

What is the underlying economic reason for the observed evolution of the influence of

inequality? The results suggest that capital-market imperfections harden the budget con-

straints in low-income countries, making any progress in education a hard task for individu-

als. In addition, entrepreneurial activity and the possibility of both the creation and the im-

37

−.5

−.4

−.3

−.2

−.1

0

.1

.2

.3

.4

.5

Mar

gina

l effe

ct o

f ine

qual

ity

5 6 7 8 9 10 11Log of initial real GDP per capita

Figure 5 The impact of inequality for different levels of development

plementation of innovations is strongly reduced if financial markets are underdeveloped. As

a high level of inequality allows some individuals to make use of their individual abilities and

hinders some others from exploiting their full potential, growth rates will be substantially

lower, especially when assuming an (approximately) equal distribution of initial cognitive

skills of children across different income levels. As the social and financial environment in

high-income countries help to ensure that the distribution of human capital endowment is

much more due to preferences and individual skills rather than initial endowment of wealth,

the influence of inequality declines. In addition, education rents in high-income countries

may even lead to growth-enhancing effect of inequality, as the incentives concerning human

capital investments, labor supply, and entrepreneurship may rise if income gaps increase.

If true, social mobility can be expected to be lower in developing countries. Data on the

correlation between the income of fathers and their sons is available in the Corak (2011)

sample. Figure A3 in the appendix illustrates that the extent of social mobility is positively

associated with the development level of the economies. The correlation of the variables is

60 percent. There is much reason to suspect that the negative impact of the Gini coefficient

in our baseline estimations may to some extent reflect the effect of equality of opportunities.

Whenever the initial wealth level of parents strongly determines the level of education a child

is able to obtain, the growth rate of the economy is lower, as growth-enhancing potentials

emanating from human capital, innovations, and entrepreneurship are wasted.

38

5 Conclusions

Evidence from a new set of fairly well comparable worldwide data shows a robust negative

correlation between income inequality and growth, as long as the transmission variables of

inequality are left open. Specifically, more unequal countries tend to have a less educated

population and higher fertility rates. Both are harmful for growth and reinforced by capital

market imperfections.

Regarding the endogenous fiscal policy channel, we have found little evidence for any

negative growth effect of public redistribution in the whole sample of countries. While

redistribution hampers investments, its negative incentive effects on average do not seem

to transmit to economic growth. The findings suggest that most of the negative effects are

offset by positive insurance effects which promote risk taking and entrepreneurship. An

alternative interpretation is that the data simply reveals that governments on average have

found the most efficient way to redistribute, whereas other possible redistribution measures

are very likely to yield inefficiencies. In fact, in combination with the resulting decrease in

net inequality, redistribution on average seems to be rather beneficial for growth, particularly

when market inequality is high.

However, these results foremost stem from developing and middle-income countries where

the negative potential of inequality is most severe due to capital market imperfections and

an inferior provision of public goods, yielding a socio-political environment where equality

of opportunities is much less pronounced than in advanced economies. When examining the

link of inequality and growth in the sample of high-income countries, we do not find any

significant correlation between inequality and growth. Policy implications may thus differ

substantially depending on the development level of the economies.

Despite its insignificance, the negative sign of the redistribution variable in most of our

estimations should not be ignored. The most growth friendly remedy for inequality is a direct

intervention at its key transmission channels. By equalizing human capital provision and

investment opportunities, governments may be able to perform the balancing act between

a reduction of net-inequality and a facilitation of economic growth most effectively. Our

findings suggest that the negative growth effects of inequality can be mitigated by public

expenditures on education.

A policy that promotes equality of opportunity enhances social mobility, which makes

an unequal distribution of incomes less severe, assuring that income differentials reflect

preferences rather than parental wealth and budget restraints. In societies with high rates

of social mobility, individuals are able to climb the income ladder through their own effort.

As such policies do not exercise negative incentive effects, indirect redistribution may be

more efficient than direct redistribution activities.

Two major limitations remain: First, it is possible that a low level of average education

and a high fertility rate are the cause, rather than the effect of inequality. While we find

evidence of a causal effect running from inequality to education and fertility, more research

39

is necessary to establish a definite conclusion about the direction of causality in a large

panel of countries. Second, the pre-post approach only measures effective redistribution

while it does not provide any insights on the policy measures through which the observed

redistribution occurs. As a next step, we will try to disentangle by which policy instruments

redistribution is achieved and how redistribution can be accomplished most efficiently in

terms of economic growth.

40

Appendix

Appendix A1: Standardization Procedure in the SWIID Our preferred measures of income

inequality and redistribution stem from the Standardized World Income Inequality Database

(SWIID, Version 4.0, released in September 2013) generated by Solt (2009). The SWIID is

based on the UN World Income Inequality Database (WIID), which is the successor of the

Deininger and Squire (1996) data set. The SWIID transforms and adjusts the WIID data in

several steps, which are best described in Solt (2009). We therefore restrict to a very brief

overview of the steps taken by Solt (2009): 1. Observations which are based on only a local

reference group and therefore not a good countrywide estimate are eliminated. 2. The data

is sorted in comparable reference units and income definitions. Plus all available data series

from the Luxembourg Income Study (LIS) are added as benchmarks. 3. Flexible ratios,

which vary over countries and over time, are calculated between the different categories

and are used to fill out missing data points. Thus 20 different time series are estimated

for each country and combined into a single variable for net income inequality. 4. Possible

measurement errors are corrected by using five-year weighted moving averages on all data

points except those taken from the LIS dataset and certain time periods.

41

0

10,000

20,000

30,000

40,000

50,000

60,000

.1 .2 .3 .4 .5 .6 .7

Inter-generative income elasticity (immobility)

Re

al p

er

Ca

pita

GD

P

Figure A3 The relationship between the intergenerational income-elasticity (immobility) and realper capita GDP

Notes: The figure reports intergenerational income elasticities which reflect estimated values of β obtainedfrom the OLS regression log yt = α + β log yt−1 + εt where yt is the income of the adult son, and yt−1

denotes the income of the father. Due to the nature of this measure, higher values of β indicate lower ratesof social mobility. The data includes values for 21 countries and can be found in Corak (2011).

42

Table

A2

Corr

elati

on

matr

ixof

the

regre

ssors

GROW

TH

log(CG

DP)

GIN

I(N)

GIN

I(M

)RED

IST

RED

IST(S)

INVS

SCHO

OLY

log(LIF

EEX)G

OVC

INFL

OPEN

PO

LRIG

HT

log(FERT)

GROW

TH

1lo

g(CG

DP)

0.0

073

1G

INI(

N)

0.0

58

-0.5

626

1G

INI(

M)

0.0

471

-0.2

914

0.7

839

1RED

IST

-0.0

328

0.5

309

-0.6

011

0.0

249

1RED

IST(S)

-0.0

604

0.6

101

-0.7

254

-0.1

578

0.9

649

1IN

VS

0.1

814

0.3

911

-0.1

858

-0.1

388

0.1

206

0.1

458

1SCHO

OLY

0.0

188

0.6

494

-0.5

801

-0.4

027

0.4

158

0.5

137

0.1

255

1lo

g(LIF

EEX)

0.0

807

0.7

106

-0.5

3-0

.3225

0.4

383

0.4

924

0.2

938

0.5

319

1G

OVC

-0.3

048

-0.3

284

-0.1

277

-0.1

128

0.0

605

0.0

782

-0.2

59

0.0

764

-0.1

137

1IN

FL

-0.3

429

-0.1

027

0.0

829

0.0

424

-0.0

79

-0.0

816

-0.1

064

-0.1

261

-0.1

449

0.1

238

1O

PEN

0.0

792

0.1

378

-0.1

062

-0.1

222

0.0

136

0.0

465

0.2

542

0.0

989

0.1

481

0.0

428

-0.0

865

1PO

LRIG

HT

0.0

432

0.5

405

-0.3

182

-0.1

328

0.3

414

0.4

241

0.1

211

0.4

384

0.5

133

-0.0

911

-0.1

083

-0.1

111

1lo

g(FERT)

-0.1

482

-0.6

795

0.6

527

0.4

539

-0.4

668

-0.5

503

-0.3

5-0

.6438

-0.6

621

-0.0

095

0.0

822

-0.2

035

-0.4

241

1TO

TR

0.1

091

0.1

423

-0.0

161

0.0

32

0.0

672

0.0

707

0.0

647

0.1

943

0.0

779

-0.0

095

-0.0

553

0.1

641

0.0

335

-0.1

835

43

References

Aghion, P., Caroli, E., and Garcia-Penalosa, C. (1999). Inequality and economic growth:The perspective of the new growth theories. Journal of Economic Literature, 37(4):1615–1660.

Aghion, P. and Howitt, P. (1992). Model of growth through creative destruction, necono-metrica.

Aghion, P. and Howitt, P. (1998). On the macroeconomic effects of major technologicalchange. In Helpman, E., editor, General Purpose Technologies and Economic Growth.MIT Press.

Aghion, P. and Howitt, P. (2002). Wage inequality and the new economy. Oxford Review ofEconomic Policy, 18(3):306–323.

Alesina, A., Ozler, S., Roubini, N., and Swagel, P. (1996). Political instability and economicgrowth. Journal of Economic growth, 1(2):189–211.

Alesina, A. and Perotti, R. (1996). Income distribution, political instability, and investment.European Economic Review, 40(6):1203–1228.

Alesina, A. and Rodrik, D. (1994). Distributive politics and economic growth. The QuarterlyJournal of Economics, 109(2):465–490.

Anderson, T. W. and Hsiao, C. (1982). Formulation and estimation of dynamic modelsusing panel data. Journal of econometrics, 18(1):47–82.

Arellano, M. and Bond, S. (1991). Some tests of specification for panel data: Monte carloevidence and an application to employment equations. The Review of Economic Studies,58(2):pp. 277–297.

Arellano, M. and Bover, O. (1995). Another look at the instrumental variable estimation oferror-components models. Journal of econometrics, 68(1):29–51.

Atkinson, A. B. and Brandolini, A. (2001). Promise and pitfalls in the use of secondarydata-sets: Income inequality in oecd countries as a case study. Journal of EconomicLiterature, 39(3):p 771–799.

Barro, R. J. (1991). Economic growth in a cross section of countries. The Quarterly Journalof Economics, 106(2):407–443.

Barro, R. J. (2000). Inequality and growth in a panel of countries. Journal of EconomicGrowth, 5(1):5–32.

Barro, R. J. (2003). Determinants of economic growth in a panel of countries. Annals ofeconomics and finance, 4:231–274.

Barro, R. J. (2008). Inequality and Growth Revisited. Working Papers on Regional EconomicIntegration 11, Asian Development Bank.

Barro, R. J. (2013). Education and economic growth. Annals of Economics and Finance,14(2):301–328.

Barro, R. J. and Lee, J. W. (2013). A new data set of educational attainment in the world,1950–2010. Journal of development economics, 104:184–198.

44

Becker, G. S. and Barro, R. J. (1988). A reformulation of the economic theory of fertility.The Quarterly Journal of Economics, 103(1):p 1–25.

Benabou, R. (1996). Inequality and growth. In NBER Macroeconomics Annual 1996,Volume 11, NBER Chapters, pages 11–92. National Bureau of Economic Research.

Berg, A., Ostry, J. D., and Tsangarides, C. G. (2014). Redistribution, Inequality, andGrowth. International Monetary Fund.

Bergh, A. (2005). On the counterfactual problem of welfare state research: How can wemeasure redistribution? European Sociological Review, 21(4):345–357.

Berthold, N. and Grundler, K. (2015). The growth crisis of germany: A blueprint of thedeveloped economies? International Economic Journal, forthcoming.

Bertola, G. (1993). Market structure and income distribution in endogenous growth models.American Economic Review, 83(2):1184–99.

Blundell, R. and Bond, S. (1998). Initial conditions and moment restrictions in dynamicpanel data models. Journal of econometrics, 87(1):115–143.

Blundell, R., Bond, S., and Windmeijer, F. (2000). Estimation in dynamic panel datamodels: improving on the performance of the standard gmm estimator (in: Boarnet,m., 1997. highways and economic productivity, interpreting recent evidence). Journal ofPlanning Literature, 11:476–486.

Bond, S., Hoeffler, A., and Temple, J. (2001). Gmm estimation of empirical growth models.(2001-W21).

Bourguignon, F. (1981). Pareto superiority of unegalitarian equilibria in stiglitz’ model ofwealth distribution with convex saving function. Econometrica, 49(6):pp. 1469–1475.

Cass, D. (1965). Optimum growth in an aggregative model of capital accumulation. TheReview of Economic Studies, 32(3):pp. 233–240.

Corak, M. (2011). Inequality from generation to generation: The united states in compari-son. Unpublished Paper. University of Ottawa.

De la Croix, D. and Doepke, M. (2003). Inequality and growth: Why differential fertilitymatters. American Economic Review, 93(4):1091–1113.

Deininger, K. and Squire, L. (1996). A new data set measuring income inequality. TheWorld Bank Economic Review, 10(3):pp. 565–591.

Diewart, W. E. and Morrison, C. J. (1986). Adjusting Output and Productivity Indexes forChanges in the Terms of Trade. Economic Journal, 96(383):659–79.

Dollar, D. and Kraay, A. (2002). Growth is good for the poor. Journal of economic growth,7(3):195–225.

Easterly, W. and Rebelo, S. (1993). Fiscal policy and economic growth. Journal of MonetaryEconomics, 32(3):417–458.

Ehrhart, C. (2009). The effects of inequality on growth: a survey of the theoretical andempirical literature.

45

Feenstra, R. C., Inklaar, R., and Timmer, M. P. (2013). The next generation of the pennworld table.

Forbes, K. J. (2000). A reassessment of the relationship between inequality and growth.The American Economic Review, 90(4):pp. 869–887.

Freedom House (2014). Freedom in the World database. Washington D.C.(US).

Galdor, O. and Zang, H. (1997). Fertility, income distribution, and economic growth: Theoryand cross-country evidence. Japan and the World Economy, 9:197–229.

Galdor, O. and Zeira, J. (1993). Income distribution and macroeconomics. The Review ofEconomic Studies, 60(1):35–52.

Grossman, G. M. and Helpman, E. (1991). Innovation and Growth in the Global Economy.MIT Press.

Gupta, D. (1990). The economics of political violence. The Effect of Political Instability onEcomic Growth. NY.

Halter, D., Oechslin, M., and Zweimuller, J. (2014). Inequality and growth: the neglectedtime dimension. Journal of Economic Growth, 19(1):81–104.

Jodice, D. A. and Taylor, C. L. (1983). The World Handbook of Political and Social Indica-tors III, 1948-1977: Aggregate data, volume 7761. ICPSR.

Joumard, Isabelle and Pisu, Mauro and Bloch, Debbie (2012). Less Income Inequalityand More Growth – Are They Compatible? Part 3. Income Redistribution via Taxes andTransfers Across OECD Countries. OECD Publishing.

Kaldor, N. (1955). Alternative theories of distribution. The Review of Economic Studies,23(2):p 83–100.

Koopmans, T. C. (1965). On the concept of optimal economic growth,” in the econometricapproach to development planning. amsterdam: North holland.

Kremer, M. and Chen, D. L. (2002). Income distribution dynamics with endogenous fertility.Journal of Economic Growth, 7(3):227–258.

Kuznets, S. (1965). Economic growth and structure: selected essays. Norton.

Li, H. and Zou, H.-f. (1998). Income inequality is not harmful for growth: Theory andevidence. Review of Development Economics, 2(3):318–334.

Lindert, P. (2004). Social Spending and Economic Gowth Since the Eighteenth Century.Cambridge University Press.

Mankiw, N. G., Romer, D., and Weil, D. N. (1992). A contribution to the empirics ofeconomic growth. The Quarterly Journal of Economics, 107(2):407–437.

Meltzer, A. H. and Richard, S. F. (1981). A rational theory of the size of government.Journal of Political Economy, 89(5):pp. 914–927.

Milanovic, B. (1999). Do more unequal countries redistribute more? does the median voterhypothesis hold? Policy Research Working Paper, (2264).

46

Mirrlees, James (1971). An exploration in the theory of optimum income taxation. TheReview of Economic Studies, 38(2):175–208.

Morand, O. (1999). Endogenous fertility, income distribution, and growth. Journal ofEconomic Growth, 4(3):331–349.

Muinelo-Gallo, L. and Roca-Sagales, O. (2013). Joint determinants of fiscal policy, incomeinequality and economic growth. Economic Modelling, 30(0):814–824.

Neves, P. and Silva, S. (2014). Inequality and growth: Uncovering the main conclusionsfrom the empirics. The Journal of Development Studies, 50(1):1–21.

Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica, 49(6):pp.1417–1426.

Okun, A. (1975). Equality and Efficiency: The Big Tradeoff. The Brookings Institution.

Panizza, U. (2002). Income inequality and economic growth: evidence from american data.Journal of Economic Growth, 7(1):25–41.

Perotti, R. (1993). Political equilibrium, income distribution, and growth. The Review ofEconomic Studies, 60(4):755–776.

Perotti, R. (1994). Income distribution and investment. European Economic Review, 38(3–4):827–835.

Perotti, R. (1996). Growth, income distribution, and democracy: What the data say. Journalof Economic Growth, 1(2):149–187.

Persson, T. and Tabellini, G. (1994). Is inequality harmful for growth? The AmericanEconomic Review, 84(3):600–621.

Piketty, T. (1997). The dynamics of the wealth distribution and the interest rate with creditrationing. Review of Economic Studies, Wiley Blackwell, 64(2):173–89.

Piketty, T. (2000). Theories of persistent inequality and intergenerational mobility. volume 1of Handbook of Income Distribution, Elsevier, pages 429–476. Elsevier.

Psacharopoulus, G. (1994). Returns to investment in education: A global update. WorldDevelopment, 22(9):187–207.

Rebelo, S. (1991). Long-run policy analysis and long-run growth. Journal of PoliticalEconomy, 99(3):500–521.

Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy,98(5):pp. S71–S102.

Roodman, D. (2009). How to do xtabond2: An introduction to difference and system gmmin stata. Stata Journal, 9(1):86.

Sinn, H.-W. (1996). Social insurance, incentives and risk taking. International Tax andPublic Finance, 3(3):259–280.

Solow, R. M. (1956). A contribution to the theory of economic growth. The QuarterlyJournal of Economics, 70(1):pp. 65–94.

47

Solt, F. (2009). Standardizing the world income inequality database*. Social Science Quar-terly, 90(2):231–242.

Soto, M. (2009). System GMM estimation with a small sample. Working Papers 395,Barcelona Graduate School of Economics.

Stiglitz, J. E. (1969). Distribution of income and wealth among individuals. Econometrica,37(3):pp. 382–397.

Stiglitz, J. E. (2012). The Price of Inequality. Penguin UK.

Tanzi, V. and Zee, H. H. (1997). Fiscal policy and long-run growth. Staff Papers - Interna-tional Monetary Fund, 44(2):179–209.

Thewissen, S. (2013). Is it the income distribution or redistribution that affects growth?Socio-Economic Review.

Van den Bosch, Karel and Cantillon, B. (2008). Policy impact. In Goodin, R., Moran, M.,and Rein, M., editors, The Oxford Handbook of Public Policy. Oxford University Press.

Vanhanen, T. (2000). A new dataset for measuring democracy, 1810-1998. Journal of PeaceResearch, 37(2):251–265.

Voitchovsky, S. (2005). Does the profile of income inequality matter for economic growth?Journal of Economic Growth, 10(3):273–296.

Woo, J., Bova, E., Kinda, T., and Zhang, Y. S. (2013). Distributional consequences of fiscalconsolidation and the role of fiscal policy: What do the data say? IMF Working Paper,13(195).

World Bank (2013). The Worldwide Governance Indicators Database (WGI). The WorldBank.

World Bank (2014). World Development Indicators Database (WDI). The World Bank.

48